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Deep learning models for digital image processing: a review

• Published: 07 January 2024
• Volume 57 , article number  11 , ( 2024 )

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• R. Archana 1 &
• P. S. Eliahim Jeevaraj 1  

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Within the domain of image processing, a wide array of methodologies is dedicated to tasks including denoising, enhancement, segmentation, feature extraction, and classification. These techniques collectively address the challenges and opportunities posed by different aspects of image analysis and manipulation, enabling applications across various fields. Each of these methodologies contributes to refining our understanding of images, extracting essential information, and making informed decisions based on visual data. Traditional image processing methods and Deep Learning (DL) models represent two distinct approaches to tackling image analysis tasks. Traditional methods often rely on handcrafted algorithms and heuristics, involving a series of predefined steps to process images. DL models learn feature representations directly from data, allowing them to automatically extract intricate features that traditional methods might miss. In denoising, techniques like Self2Self NN, Denoising CNNs, DFT-Net, and MPR-CNN stand out, offering reduced noise while grappling with challenges of data augmentation and parameter tuning. Image enhancement, facilitated by approaches such as R2R and LE-net, showcases potential for refining visual quality, though complexities in real-world scenes and authenticity persist. Segmentation techniques, including PSPNet and Mask-RCNN, exhibit precision in object isolation, while handling complexities like overlapping objects and robustness concerns. For feature extraction, methods like CNN and HLF-DIP showcase the role of automated recognition in uncovering image attributes, with trade-offs in interpretability and complexity. Classification techniques span from Residual Networks to CNN-LSTM, spotlighting their potential in precise categorization despite challenges in computational demands and interpretability. This review offers a comprehensive understanding of the strengths and limitations across methodologies, paving the way for informed decisions in practical applications. As the field evolves, addressing challenges like computational resources and robustness remains pivotal in maximizing the potential of image processing techniques.

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1 Introduction

Image Processing (IP) stands as a multifaceted field encompassing a range of methodologies dedicated to gleaning valuable insights from images. Concurrently, the landscape of Artificial Intelligence (AI) has burgeoned into an expansive realm of exploration, serving as the conduit through which intelligent machines strive to replicate human cognitive capacities. Within the expansive domain of AI, Machine Learning (ML) emerges as a pivotal subset, empowering models to autonomously extrapolate outcomes from structured datasets, effectively diminishing the need for explicit human intervention in the decision-making process. At the heart of ML lies Deep Learning (DL), a subset that transcends conventional techniques, particularly in handling unstructured data. DL boasts an unparalleled potential for achieving remarkable accuracy, at times even exceeding human-level performance. This prowess, however, hinges on the availability of copious data to train intricate neural network architectures, characterized by their multilayered composition. Unlike their traditional counterparts, DL models exhibit an innate aptitude for feature extraction, a task that historically posed challenges. This proficiency can be attributed to the architecture's capacity to inherently discern pertinent features, bypassing the need for explicit feature engineering. Rooted in the aspiration to emulate cognitive processes, DL strives to engineer learning algorithms that faithfully mirror the intricacies of the human brain. In this paper, a diverse range of deep learning methodologies, contributed by various researchers, is elucidated within the context of Image Processing (IP) techniques.

This comprehensive compendium delves into the diverse and intricate landscape of Image Processing (IP) techniques, encapsulating the domains of image restoration, enhancement, segmentation, feature extraction, and classification. Each domain serves as a cornerstone in the realm of visual data manipulation, contributing to the refinement, understanding, and utilization of images across a plethora of applications.

Image restoration techniques constitute a critical first step in rectifying image degradation and distortion. These methods, encompassing denoising, deblurring, and inpainting, work tirelessly to reverse the effects of blurring, noise, and other forms of corruption. By restoring clarity and accuracy, these techniques lay the groundwork for subsequent analyses and interpretations, essential in fields like medical imaging, surveillance, and more.

The purview extends to image enhancement, where the focus shifts to elevating image quality through an assortment of adjustments. Techniques that manipulate contrast, brightness, sharpness, and other attributes enhance visual interpretability. This enhancement process, applied across diverse domains, empowers professionals to glean finer details, facilitating informed decision-making and improved analysis.

The exploration further extends to image segmentation, a pivotal process for breaking down images into meaningful regions. Techniques such as clustering and semantic segmentation aid in the discernment of distinct entities within images. The significance of image segmentation is particularly pronounced in applications like object detection, tracking, and scene understanding, where it serves as the backbone of accurate identification and analysis.

Feature extraction emerges as a fundamental aspect of image analysis, entailing the identification of crucial attributes that pave the way for subsequent investigations. While traditional methods often struggle to encapsulate intricate attributes, deep learning techniques excel in autonomously recognizing complex features, contributing to a deeper understanding of images and enhancing subsequent analysis.

Image classification, a quintessential task in the realm of visual data analysis, holds prominence. This process involves assigning labels to images based on their content, playing a pivotal role in areas such as object recognition and medical diagnosis. Both machine learning and deep learning techniques are harnessed to automate the accurate categorization of images, enabling efficient and effective decision-making.

The Sect.  1 elaborates the insights of the image processing operations. In Sect.  2 of this paper, a comprehensive overview of the evaluation metrics employed for various image processing operations is provided. Moving to Sect.  3 , an in-depth exploration unfolds concerning the diverse range of Deep Learning (DL) models specifically tailored for image preprocessing tasks. Within Sect.  4 , a thorough examination ensues, outlining the array of DL methods harnessed for image segmentation tasks, unraveling their techniques and applications.

Venturing into Sect.  5 , a meticulous dissection is conducted, illuminating DL strategies for feature extraction, elucidating their significance and effectiveness. In Sect.  6 , the spotlight shifts to DL models designed for the intricate task of image classification, delving into their architecture and performance characteristics. The significance of each models are discussed in Sect.  7 . Concluding this comprehensive analysis, Sect.  8 encapsulates the synthesized findings and key takeaways, consolidating the insights gleaned from the study.

The array of papers discussed in this paper collectively present a panorama of DL methodologies spanning various application domains. Notably, these domains encompass medical imagery, satellite imagery, botanical studies involving flower images, as well as fruit images, and even real-time image scenarios. Each domain's unique challenges and intricacies are met with tailored DL approaches, underscoring the adaptability and potency of these methods across diverse real-world contexts.

2 Metrics for image processing operations

Evaluation metrics serve as pivotal tools in the assessment of the efficacy and impact of diverse image processing techniques. These metrics serve the essential purpose of furnishing quantitative measurements that empower researchers and practitioners to undertake an unbiased analysis and facilitate meaningful comparisons among the outcomes yielded by distinct methods. By employing these metrics, the intricate and often subjective realm of image processing can be rendered more objective, leading to informed decisions and advancements in the field.

2.1 Metrics for image preprocessing

2.1.1 mean squared error (mse).

The average of the squared differences between predicted and actual values. It penalizes larger errors more heavily.

where, M and N are the dimensions of the image. \({Original}_{(i,j)}\,and\, {Denoised}_{(i,j)}\) are the pixel values at position (i, j) in the original and denoised images respectively.

2.1.2 Peak signal-to-noise ratio (PSNR)

PSNR is commonly used to measure the quality of restored images. It compares the original and restored images by considering the mean squared error between their pixel values.

where, MAX is the maximum possible pixel value (255 for 8-bit images), MSE is the mean squared error between the original and denoised images.

2.1.3 Structural similarity index (SSIM)

SSIM is applicable to image restoration as well. It assesses the similarity between the original and restored images in terms of luminance, contrast, and structure. Higher SSIM values indicate better restoration quality.

\({SSIM}_{\left(x,y\right)}=\left(2*{\mu }_{x }*{\mu }_{y }+{c}_{1}\right)*(2*{\sigma }_{xy }+{c}_{2})/({\mu }_{x}^{2}+{\mu }_{y}^{2}+{c}_{1})*({\sigma }_{x}^{2}+{\sigma }_{y}^{2}+{c}_{2}\) ).where, \({\mu }_{x }and {\mu }_{y}\) are the mean values of the original and denoised images. \({\sigma }_{x}^{2} and {\sigma }_{y}^{2}\) are the variances of the original and denoised images. \({\sigma }_{xy}\) is the covariance between the original and denoised images. \({c}_{1}{ and c}_{2}\) are constants to avoid division by zero.

2.1.4 Mean structural similarity index (MSSIM)

MSSIM extends SSIM to multiple patches of the image and calculates the mean SSIM value over those patches.

where x i and y i are the patches of the original and enhanced images.

2.1.5 Mean absolute error (MAE)

The average of the absolute differences between predicted and actual values. It provides a more robust measure against outliers.

where n is the number of samples.

2.1.6 NIQE (Naturalness image quality evaluator)

NIQE quantifies the naturalness of an image by measuring the deviation of local statistics from natural images. It calculates the mean of the local differences in luminance and contrast.

2.1.7 FID (Fréchet inception distance)

FID measures the distance between two distributions (real and generated images) using the Fréchet distance between their feature representations calculated by a pre-trained neural network.

2.2 Metrics for image segmentation

2.2.1 intersection over union (iou).

IoU measures the overlap between the predicted bounding box and the ground truth bounding box. Commonly used to evaluate object detection models.

2.2.2 Average precision (AP)

AP measures the precision at different recall levels and computes the area under the precision-recall curve. Used to assess object detection and instance segmentation models.

2.2.3 Dice similarity coefficient

The Dice similarity coefficient is another measure of similarity between the predicted segmentation and ground truth. It considers both false positives and false negatives.

The Dice Similarity Coefficient, also known as the Sørensen-Dice coefficient, is a common metric for evaluating the similarity between two sets. In the context of image segmentation, it quantifies the overlap between the predicted segmentation and the ground truth, taking into account both true positives and false positives. DSC ranges from 0 to 1, where higher values indicate better overlap between the predicted and ground truth segmentations. A DSC of 1 corresponds to a perfect match.

2.2.4 Average accuracy (AA)

Average Accuracy measures the overall accuracy of the segmentation by calculating the percentage of correctly classified pixels across all classes.

where, N is the number of classes. True Positives i and True Negativesi are the true positives and true negatives for class ii. Total Pixels i is the total number of pixels in class.

2.3 Metrics for feature extraction and classification

2.3.1 accuracy.

The ratio of correctly predicted instances to the total number of instances. It's commonly used for balanced datasets but can be misleading for imbalanced datasets.

2.3.2 Precision

The ratio of true positive predictions to the total number of positive predictions. It measures the model’s ability to avoid false positives.

2.3.3 Recall (Sensitivity or true positive rate)

The ratio of true positive predictions to the total number of actual positive instances. It measures the model’s ability to correctly identify positive instances.

2.3.4 F1-Score

The harmonic mean of precision and recall. It provides a balanced measure between precision and recall.

2.3.5 Specificity (True negative rate)

The ratio of true negative predictions to the total number of actual negative instances.

2.3.6 ROC curve (Receiver operating characteristic curve )

A graphical representation of the trade-off between true positive rate and false positive rate as the classification threshold varies. These metrics are commonly used in binary classification. The ROC curve plots this trade-off, and AUC summarizes the curve's performance.

3 Image preprocessing

Image preprocessing is a fundamental step in the field of image processing that involves a series of operations aimed at preparing raw or unprocessed images for further analysis, interpretation, or manipulation. This crucial phase helps enhance the quality of images, mitigate noise, correct anomalies, and extract relevant information, ultimately leading to more accurate and reliable results in subsequent tasks such as image analysis, recognition, and classification.

Image preprocessing is broadly categorized into image restoration which removes the noises and blurring in the images and image enhancement which improves the contrast, brightness and details of the images.

3.1 Image restoration

Image restoration serves as a pivotal process aimed at reclaiming the integrity and visual quality of images that have undergone degradation or distortion. Its objective is to transform a degraded image into a cleaner, more accurate representation, thereby revealing concealed details that may have been obscured. This process is particularly vital in scenarios where images have been compromised due to factors like digital image acquisition issues or post-processing procedures such as compression and transmission. By rectifying these issues, image restoration contributes to enhancing the interpretability and utility of visual data.

A notable adversary in the pursuit of pristine images is noise, an unintended variation in pixel values that introduces unwanted artifacts and can lead to the loss of important information. Different types of noise, such as Gaussian noise characterized by its random distribution, salt and pepper noise causing sporadic bright and dark pixels, and speckle noise resulting from interference, can mar the quality of images. These disturbances often originate from the acquisition process or subsequent manipulations of the image data.

Historically, traditional image restoration techniques have included an array of methods to mitigate the effects of degradation and noise. These techniques encompass constrained least square filters, blind deconvolution methods that aim to reverse the blurring effects, Weiner and inverse filters for enhancing signal-to-noise ratios, as well as Adaptive Mean, Order Static, and Alpha-trimmed mean filters that tailor filtering strategies based on the local pixel distribution. Additionally, algorithms dedicated to deblurring counteract motion or optical-induced blurriness, restoring sharpness. Denoising techniques (Tian et al. 2018 ; Peng et al. March 2020 ; Tian and Fei 2020 ) such as Total Variation Denoising (TVD) and Non-Local Means (NLM) further contribute by effectively reducing random noise while preserving essential image details, collectively advancing the field's capacity to improve image integrity and visual clarity. In Table 1 , a summary of deep learning models for image restoration is provided, including their respective advantages and disadvantages.

Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNN), have revolutionized the field of image restoration. CNNs are adept at learning and extracting complex features from images, allowing them to recognize patterns and nuances that may be challenging for traditional methods to discern. Through extensive training on large datasets, these networks can significantly enhance the quality of restored images, often surpassing the capabilities of conventional techniques. This leap in performance is attributed to the network's ability to implicitly understand the underlying structures of images and infer optimal restoration strategies.

Chunwei Tiana et al. (Tian and Fei 2020 ) provided an overview of deep network utilization in denoising images to eliminate Gaussian noise. They explored deep learning techniques for various noisy tasks, including additive white noisy images, blind denoising, and real noisy images. Through benchmark dataset analysis, they assessed the denoising outcomes, efficiency, and visual effects of distinct networks, followed by cross-comparisons of different image denoising methods against diverse types of noise. They concluded by addressing the challenges encountered by deep learning in image denoising.

Quan et al. ( 2020 ) introduced a self-supervised deep learning method named Self2Self for image denoising. Their study demonstrated that the denoising neural network trained with the Self2Self scheme outperformed non-learning-based denoisers and single-image-learning denoisers.

Yan et al. ( 2020 ) proposed a novel technique for removing speckle noise in digital holographic speckle pattern interferometry (DHSPI) wrapped phase. Their method employed improved denoising convolutional neural networks (DnCNNs) and evaluated noise reduction using Mean Squared Error (MSE) comparisons between noisy and denoised data.

Sori et al. ( 2021 ) presented lung cancer detection from denoised Computed Tomography images using a two-path convolutional neural network (CNN). They employed the denoised image by DR-Net as input for lung cancer detection, achieving superior results in accuracy, sensitivity, and specificity compared to recent approaches.

Pang et al. ( 2021 ) implemented an unsupervised deep learning method for denoising using unmatched noisy images, with a loss function analogous to supervised training. Their model, based on the Additive White Gaussian Noise model, attained competitive outcomes against unsupervised methods.

Hasti and Shin ( 2022 ) proposed a deep learning approach to denoise fuel spray images derived from Mie scattering and droplet center detection. A comprehensive comparison of diverse algorithms—standard CNN, modified ResNet, and modified U-Net—revealed the superior performance of the modified U-Net architecture in terms of Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR).

Niresi and Chi et al. ( 2022 ) employed an unsupervised HSI denoising algorithm under the DIP framework, which minimized the Half-Quadratic Lagrange Function (HLF) without regularizers, effectively removing mixed types of noises like Gaussian noise and sparse noise while preserving edges. Zhou et al. ( 2022 ) introduced a novel bearing fault diagnosis model called deep network-based sparse denoising (DNSD). They addressed the challenges faced by traditional sparse theory algorithms, demonstrating that DNSD overcomes issues related to generalization, parameter adjustment, and data-driven complexity. Tawfik et al. ( 2022 ) conducted a comprehensive evaluation of image denoising techniques, categorizing them as traditional (user-based) non-learnable denoising filters and DL-based methods. They introduced semi-supervised denoising models and employed qualitative and quantitative assessments to compare denoising performance. Meng and Zhang et al. ( 2022 ) proposed a gray image denoising method utilizing a constructed symmetric and dilated convolutional residual network. Their technique not only effectively removed noise in high-noise settings but also achieved higher SSIM, PSNR, FOM, and improved visual effects, offering valuable data for subsequent applications like target detection, recognition, and tracking.

In essence, image restoration encapsulates a continuous endeavor to salvage and improve the visual fidelity of images marred by degradation and noise. As technology advances, the integration of deep learning methodologies promises to propel this field forward, ushering in new standards of image quality and accuracy.

3.2 Image enhancement

Image enhancement refers to the process of manipulating an image to improve its visual quality and interpretability for human perception. This technique involves various adjustments that aim to reveal hidden details, enhance contrast, and sharpen edges, ultimately resulting in an image that is clearer and more suitable for analysis or presentation. The goal of image enhancement is to make the features within an image more prominent and recognizable, often by adjusting brightness, contrast, color balance, and other visual attributes.

Standard image enhancement methods encompass a range of techniques, including histogram matching to adjust the pixel intensity distribution, contrast-limited adaptive histogram equalization (CLAHE) to enhance local contrast, and filters like the Wiener filter and median filter to reduce noise. Linear contrast adjustment and unsharp mask filtering are also commonly employed to boost image clarity and sharpness.

In recent years, deep learning methods have emerged as a powerful approach for image enhancement. These techniques leverage large datasets and complex neural network architectures to learn patterns and features within images, enabling them to restore and enhance images with impressive results. Researchers have explored various deep learning models for image enhancement, each with its strengths and limitations. These insights are summarized in Table 2 .

The study encompasses an array of innovative techniques, including the integration of Retinex theory and deep image priors in the Novel RetinexDIP method, robustness-enhancing Fuzzy operation to mitigate overfitting, and the fusion of established techniques like Unsharp Masking, High-Frequency Emphasis Filtering, and CLAHE with EfficientNet-B4, ResNet-50, and ResNet-18 architectures to bolster generalization and robustness. Among these, FCNN Mean Filter exhibits computational efficiency, while CV-CNN leverages the capabilities of complex-valued convolutional networks. Additionally, the versatile pix2pixHD framework and the swift convergence of LE-net (Light Enhancement Net) contribute to the discourse. Deep Convolutional Neural Networks demonstrate robust enhancements, yet require meticulous hyperparameter tuning. Finally, MSSNet-WS (Multi-Scale-Stage Network) efficiently converges and addresses overfitting. This analysis systematically highlights their merits, encompassing improved convergence rates, overfitting mitigation, robustness, and computational efficiency.

Gao et al. ( 2022 ) proposed an inventive approach for enhancing low-light images by leveraging Retinex decomposition after initial denoising. In their method, the Retinex decomposition technique was applied to restore brightness and contrast, resulting in images that are clearer and more visually interpretable. Notably, their method underwent rigorous comparison with several other techniques, including LIME, NPE, SRIE, KinD, Zero-DCE, and RetinexDIP, showcasing its superior ability to enhance image quality while preserving image resolution and minimizing memory usage (Tables  1 , 2 , 3 , 4 and 5 ).

Liu et al. ( 2019 ) explored the application of deep learning in iris recognition, utilizing Fuzzy-CNN (F-CNN) and F-Capsule models. What sets their approach apart is the integration of Gaussian and triangular fuzzy filters, a novel enhancement step that contributes to improving the clarity of iris images. The significance lies in the method’s practicality, as it smoothly integrates with existing networks, offering a seamless upgrade to the recognition process.

Munadi et al. ( 2020 ) combined deep learning techniques with image enhancement methodologies to tackle tuberculosis (TB) image classification. Their innovative approach involved utilizing Unsharp Masking (UM) and High-Frequency Emphasis Filtering (HEF) in conjunction with EfficientNet-B4, ResNet-50, and ResNet-18 models. By evaluating the performance of three image enhancement algorithms, their work demonstrated remarkable accuracy and Area Under Curve (AUC) scores, revealing the potential of their method for accurate TB image diagnosis.

Lu et al. ( 2021 ) introduced a novel application of deep learning, particularly the use of a fully connected neural network (FCNN), to address impulse noise in degraded images with varying noise densities. What's noteworthy about their approach is the development of an FCNN mean filter that outperformed traditional mean/median filters, especially when handling low-noise density environments. Their study thus highlights the promising capabilities of deep learning in noise reduction scenarios. Quan et al. ( 2020 ) presented a non-blind image deblurring technique employing complex-valued CNN (CV-CNN). The uniqueness of their approach lies in incorporating Gabor-domain denoising as a prior step in the deconvolution model. By evaluating their model using quantitative metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), their work showcased effective deblurring outcomes, reaffirming the potential of complex-valued CNNs in image restoration.

Jin et al. ( 2021 ) harnessed the power of deep learning, specifically the pix2pixHD model, to enhance multidetector computed tomography (MDCT) images. Their focus was on accurately measuring vertebral bone structure. By utilizing MDCT images, their approach demonstrated the potential of deep learning techniques in precisely enhancing complex medical images, which can play a pivotal role in accurate clinical assessments.

Li et al. ( 2021a ) introduced a CNN-based LE-net tailored for image recovery in low-light conditions, catering to applications like driver assistance systems and connected autonomous vehicles (CAV). Their work highlighted the significance of their model in outperforming traditional approaches and even other deep learning models. The research underscores the importance of tailored solutions for specific real-world scenarios.

Mehranian et al. ( 2022 ) ventured into the realm of Time-of-Flight (ToF) enhancement in positron emission tomography (PET) images using deep convolutional neural networks. Their innovative use of the block-sequential-regularized-expectation–maximization (BSREM) algorithm for PET data reconstruction in combination with DL-ToF(M) demonstrated superior diagnostic performance, measured through metrics like SSIM and Fréchet Inception Distance (FID).

Kim et al. ( 2022 ) introduced the Multi-Scale-Stage Network (MSSNet), a pioneering deep learning-based approach for single image deblurring. What sets their work apart is their meticulous analysis of previous deep learning-based coarse-to-fine approaches, leading to the creation of a network that achieves state-of-the-art performance in terms of image quality, network size, and computation time.

In the core, image enhancement plays a crucial role in improving the visual quality of images, whether for human perception or subsequent analytical tasks. The combination of traditional methods and cutting-edge deep learning techniques continues to advance our ability to reveal and amplify important information within images. Each of these studies contributes to the expanding landscape of image enhancement and restoration, showcasing the immense potential of deep learning techniques in various domains, from medical imaging to low-light scenarios, while addressing specific challenges and advancing the state-of-the-art in their respective fields.

However, the study recognizes inherent limitations, including constrained adaptability, potential loss of intricate details, and challenges posed by complex scenes or real-world images. Through a meticulous exploration of these advantages and disadvantages, the study endeavors to offer a nuanced perspective on the diverse applicability of these methodologies across various image enhancement scenarios.

4 Image segmentation

Image segmentation is a pivotal process that involves breaking down an image into distinct segments based on certain discernible characteristics such as intensity, color, texture, or spatial proximity. This technique is classified into two primary categories: Semantic segmentation and Instance segmentation. Semantic segmentation assigns each pixel to a specific class within the input image, enabling the identification of distinct object regions. On the other hand, instance segmentation takes a step further by not only categorizing pixels into classes but also differentiating individual instances of those classes within the image.

Traditional segmentation methodologies entail the partitioning of data, such as images, into well-defined segments governed by predetermined criteria. This approach predates the era of deep learning and relies on techniques rooted in expert-designed features or domain-specific knowledge. Common techniques encompass thresholding, which categorizes pixels into object and background regions using specific intensity thresholds, region-based segmentation that clusters pixels with similar attributes into coherent regions, and edge detection to identify significant intensity transitions that might signify potential boundaries.Nonetheless, traditional segmentation techniques grapple with inherent complexities when it comes to handling intricate shapes, dynamic backgrounds, and noise within the data. Moreover, the manual craftsmanship of features for various scenarios can be laborious and might not extend well to different contexts. In contrast, deep learning has ushered in a paradigm shift in segmentation by introducing automated feature learning. Deep neural networks have the remarkable ability to extract intricate features directly from raw data, negating the necessity for manual feature engineering. This empowers them to capture nuanced spatial relationships and adapt to variations, effectively addressing the limitations inherent in traditional methods. This transformation, especially pronounced in image segmentation tasks, has opened doors to unprecedented possibilities in the field of computer vision and image analysis. Table 3 encapsulates the strengths and limitations of various explored deep learning models.

Ahmed et al. ( 2020 ) conducted a comprehensive exploration of deep learning-based semantic segmentation models for the challenging task of top-view multiple person segmentation. They assessed the performance of key models, including Fully Convolutional Neural Network (FCN), U-Net, and DeepLabV3. This investigation is particularly important as accurate segmentation of multiple individuals in top-view images holds significance in various applications like surveillance, crowd monitoring, and human–computer interaction. The researchers found that DeepLabV3 and U-Net outperformed FCN in terms of accuracy. These models achieved impressive accuracy and mean Intersection over Union (mIoU) scores, indicating the precision of segmentation, with DeepLabV3 and U-Net leading the way. The results underscore the value of utilizing advanced deep learning models for complex segmentation tasks involving multiple subjects.

Wang et al. ( 2020 ) proposed an adaptive segmentation algorithm employing the UNet structure, which is adept at segmenting both shallow and deep features. Their study addressed the challenge of segmenting complex boundaries within images, a crucial task in numerous medical imaging and computer vision applications. They validated their model's effectiveness on natural scene images and liver cancer CT images, highlighting its advantages over existing segmentation methods. This research contributes to the field by showcasing the potential of adaptive segmentation algorithms, emphasizing their superiority in handling intricate boundaries in diverse image datasets.

Ahammad et al. ( 2020 ) introduced a novel deep learning framework based on Convolutional Neural Networks (CNNs) for diagnosing Spinal Cord Injury (SCI) features through segmentation. This study's significance lies in its application to medical imaging, specifically spinal cord disease prediction. Their model’s high computational efficiency and remarkable accuracy underscore its potential clinical utility. The CNN-based framework leveraged sensor SCI image data, demonstrating the capacity of deep learning to contribute to accurate diagnosis and prediction in medical scenarios, enhancing patient care.

Lorenzoni et al. ( 2020 ) employed Deep Learning techniques based on Convolutional Neural Networks (CNNs) to automate the segmentation of microCT images of distinct cement-based composites. This research is essential in materials science and civil engineering, where automated segmentation can aid in understanding material properties. Their study emphasizes the adaptability of Deep Learning models, showcasing the transferability of network parameters optimized on high-strength materials to other related contexts. This work demonstrates the potential of CNN-based methodologies for advancing materials characterization and analysis.

Mahajan et al. ( 2021 ) introduced a clustering-based profound iterating Deep Learning model (CPIDM) for hyperspectral image segmentation. This research addresses the challenge of segmenting hyperspectral images, which are prevalent in fields like remote sensing and environmental monitoring. The proposed approach's superiority over state-of-the-art methods indicates its potential for enhancing the accuracy of hyperspectral image analysis. The study contributes to the field by providing an innovative methodology to tackle the unique challenges posed by hyperspectral data.

Jalali et al. ( 2021 ) designed a novel deep learning-based approach for segmenting lung regions from CT images using Bi-directional ConvLSTM U-Net with densely connected convolutions (BCDU-Net). This research is critical for medical image analysis, specifically lung-related diagnoses. Their model's impressive accuracy on a large dataset indicates its potential for aiding radiologists in identifying lung regions accurately. The application of advanced deep learning architectures to medical imaging tasks underscores the transformative potential of such technologies in healthcare.

Bouteldja et al. ( 2020 ) developed a CNN-based approach for accurate multiclass segmentation of stained kidney images from various species and renal disease models. This research’s significance lies in its potential contribution to histopathological analysis and disease diagnosis. The model's high performance across diverse species and disease models highlights its robustness and utility for aiding pathologists in accurate image-based diagnosis.

Liu et al. ( 2021 ) proposed a novel convolutional neural network architecture incorporating cross-connected layers and multi-scale feature aggregation for image segmentation. The research addresses the need for advanced segmentation techniques that can capture intricate features and relationships within images. Their model's impressive performance metrics underscore its potential for enhancing segmentation accuracy, which is pivotal in diverse fields, including medical imaging, robotics, and autonomous systems.

Saood and Hatem et al. ( 2021 ) introduced deep learning networks, SegNet and U-Net, for segmenting COVID-19-infected areas in CT scan images. This research's timeliness is evident, as it contributes to the fight against the global pandemic. Their comparison of network performance provides insights into the effectiveness of different deep learning architectures for accurately identifying infected regions in lung images. This work showcases the agility of deep learning in addressing real-world challenges.

Nurmain et al. ( 2020 ), a novel approach employing Mask-RCNN is introduced for accurate fetal septal defect detection. Addressing limitations in previous methods, the model demonstrates multiclass heart chamber detection with high accuracy: right atrium (97.59%), left atrium (99.67%), left ventricle (86.17%), right ventricle (98.83%), and aorta (99.97%). Competitive results are shown for defect detection in atria and ventricles, with MRCNN achieving around 99.48% mAP compared to 82% for FRCNN. The study concludes that the proposed MRCNN model holds promise for aiding cardiologists in early fetal congenital heart disease screening.

Park et al. ( 2021a ) propose a method for intelligently segmenting food in images using deep neural networks. They address labor-intensive data collection by utilizing synthetic data through 3D graphics software Blender, training Mask R-CNN for instance segmentation. The model achieves 52.2% on real-world food instances with only synthetic data, and + 6.4%p performance improvement after fine-tuning compared to training from scratch. Their approach shows promise for healthcare robot systems like meal assistance robots.

Pérez-Borrero et al. ( 2020 ) underscores the significance of fruit instance segmentation, specifically within autonomous fruit-picking systems. It highlights the adoption of deep learning techniques, particularly Mask R-CNN, as a benchmark. The review justifies the proposed methodology's alterations to address limitations, emphasizing its efficiency gains. Additionally, the introduction of the Instance Intersection Over Union (I2oU) metric and the StrawDI_Db1 dataset creation are positioned as contributions with real-world implementation potential.

These studies collectively highlight the transformative impact of deep learning in various segmentation tasks, ranging from medical imaging to materials science and computer vision. By leveraging advanced neural network architectures and training methodologies, researchers are pushing the boundaries of what is achievable in image segmentation, ultimately contributing to advancements in diverse fields and applications.

5 Feature extraction

Feature extraction is a fundamental process in image processing and computer vision that involves transforming raw pixel data into a more compact and informative representation, often referred to as features. These features capture important characteristics of the image, making it easier for algorithms to understand and analyze images for various tasks like object recognition, image classification, and segmentation. Traditional methods of feature extraction were prevalent before the rise of deep learning and involved techniques that analyzed pixel-level information.Some traditional methods are explained here. Principle Components Analysis (PCA) is a statistical technique that reduces the dimensionality of the data while retaining as much of the original variance as possible. It identifies the orthogonal axes (principal components) along which the data varies the most. Independent Component Analysis (ICA) aims to find a linear transformation of the data into statistically independent components. It is often used for separating mixed sources in images, such as separating different image sources from a single mixed image. Locally Linear Embedding (LLE) is a nonlinear dimensionality reduction technique that aims to preserve the local structure of data points. It finds a low-dimensional representation of the data while maintaining the neighborhood relationships.

These traditional methods of feature extraction have been widely used and have provided valuable insights and representations for various image analysis tasks. However, they often rely on handcrafted features designed by experts or domain knowledge, which can be labor-intensive and may not generalize well across different types of images or tasks.

Conventional methods of feature extraction encompass the conversion of raw data into a more concise and insightful representation by pinpointing specific attributes or characteristics. These selected features are chosen to encapsulate vital insights and patterns inherent in the data. This procedure often involves a manual approach guided by domain expertise or specific insights. For example, within image processing, methods like Histogram of Oriented Gradients (HOG) might extract insights about gradient distributions, while in text analysis, features such as word frequencies could be selected.

Despite the effectiveness of traditional feature extraction for particular tasks and its ability to provide data insights, it comes with inherent limitations. Conventional techniques frequently necessitate expert intervention to craft features, which can be a time-intensive process and might overlook intricate relationships or patterns within the data. Moreover, traditional methods might encounter challenges when dealing with data of high dimensionality or scenarios where features are not easily definable.

In contrast, the ascent of deep learning approaches has revolutionized feature extraction by automating the process. Deep neural networks autonomously learn to extract meaningful features directly from raw data, eliminating the need for manual feature engineering. This facilitates the capture of intricate relationships, patterns, and multifaceted interactions that traditional methods might overlook. Consequently, deep learning has showcased exceptional achievements across various domains, particularly in tasks involving intricate data, such as image and speech recognition. Table 4 succinctly outlines the metrics, strengths and limitations of diverse deep learning models explored for feature enhancement.

Magsi et al. ( 2020 ) embarked on a significant endeavor in the realm of disease identification within date palm trees by harnessing the power of deep learning techniques. Their study centered around texture and color extraction methods from images of various date palm diseases. Through the application of Convolutional Neural Networks (CNNs), they effectively created a system that could discern diseases based on specific visual patterns. The achieved accuracy of 89.4% signifies the model's proficiency in accurately diagnosing diseases within this context. This approach not only showcases the potential of deep learning in addressing agricultural challenges but also emphasizes the importance of automated disease detection for crop management and security.

Sharma et al. ( 2020 ) delved into the domain of medical imaging with a focus on chest X-ray images. They introduced a comprehensive investigation involving different deep Convolutional Neural Network (CNN) architectures to facilitate the extraction of features from these images. Notably, the study evaluated the impact of dataset size on CNN performance, highlighting the scalability of their approach. By incorporating augmentation and dropout techniques, the model achieved a high accuracy of 0.9068, suggesting its ability to accurately classify and diagnose chest X-ray images. This work underscores the potential of deep learning in aiding medical professionals in diagnosing diseases and conditions through image analysis.

Zhang et al. ( 2020 ) offered a novel solution to the challenge of distinguishing between genuine and counterfeit facial images generated using deep learning methods. Their approach relied on a Counterfeit Feature Extraction Method that employed a Convolutional Neural Network (CNN) model. This model demonstrated remarkable accuracy, achieving a rate of 97.6%. Beyond the impressive accuracy, the study also addressed a crucial aspect of computational efficiency, highlighting the potential for reducing the computational demands associated with counterfeit image detection. This research is particularly relevant in today's digital landscape where ensuring the authenticity of images has become increasingly vital.

Simon and V et al. ( 2020 ) explored the fusion of deep learning and feature extraction in the context of image classification and texture analysis. Their study involved Convolutional Neural Networks (CNNs) including popular architectures like AlexNet, VGG19, Inception, InceptionResNetV3, ResNet, and DenseNet201. These architectures were employed to extract meaningful features from images, which were then fed into a Support Vector Machine (SVM) for texture classification. The results were promising, with the model achieving good to superior accuracy levels ranging from 85 to 95% across different pretrained models and datasets. This approach showcases the ability of deep learning to contribute to image analysis tasks, particularly when combined with traditional machine learning techniques.

Sungheetha and Sharma et al. ( 2021 ) addressed the critical challenge of detecting diabetic conditions through the identification of specific signs within blood vessels of the eye. Their approach relied on a deep feature Convolutional Neural Network (CNN) designed to spot these indicators. With an impressive accuracy of 97%, the model demonstrated its efficacy in accurately identifying diabetic conditions. This work not only showcases the potential of deep learning in medical diagnostics but also highlights its ability to capture intricate visual patterns that are indicative of specific health conditions.

Devulapalli et al. ( 2021 ) proposed a hybrid feature extraction method that combined Gabor transform-based texture features with automated high-level features using the Googlenet architecture. By utilizing pre-trained models such as Alexnet, VGG 16, and Googlenet, the study achieved exceptional accuracy levels. Interestingly, the hybrid feature extraction method outperformed the existing pre-trained models, underscoring the potential of combining different feature extraction techniques to achieve superior performance in image analysis tasks. Shankar et al. ( 2022 ) embarked on the critical task of COVID-19 diagnosis using chest X-ray images. Their approach involved a multi-step process that encompassed preprocessing through Weiner filtering, fusion-based feature extraction using GLCM, GLRM, and LBP, and finally, classification through an Artificial Neural Network (ANN). By carefully selecting optimal feature subsets, the model exhibited the potential for robust classification between infected and healthy patients. This study showcases the versatility of deep learning in medical diagnostics, particularly in addressing urgent global health challenges.

Ahmad et al. ( 2022 ) made significant strides in breast cancer detection by introducing a hybrid deep learning model, AlexNet-GRU, capable of autonomously extracting features from the PatchCamelyon benchmark dataset. The model demonstrated its prowess in accurately identifying metastatic cancer in breast tissue. With superior performance compared to state-of-the-art methods, this research emphasizes the potential of deep learning in medical imaging, specifically for cancer detection and classification. Sharif et al. ( 2019 ) ventured into the complex field of detecting gastrointestinal tract (GIT) infections using wireless capsule endoscopy (WCE) images. Their innovative approach combined deep convolutional (CNN) and geometric features to address the intricate challenges posed by lesion attributes. The fusion of contrast-enhanced color features and geometric characteristics led to exceptional classification accuracy and precision, showcasing the synergy between deep learning and traditional geometric features. This approach is particularly promising in enhancing medical diagnostics through the integration of multiple information sources.

Aarthi and Rishma ( 2023 ) responded to the pressing challenges of waste management by introducing a real-time automated waste detection and segregation system using deep learning. Leveraging the Mask R-CNN architecture, their model demonstrated the capability to identify and classify waste objects in real time. Additionally, the study explored the extraction of geometric features for more effective object manipulation by robotic arms. This innovative approach not only addresses environmental concerns related to waste but also showcases the potential of deep learning in practical applications beyond traditional image analysis, with the aim of enhancing efficiency and reducing pollution risks.

These studies showcase the efficacy of methods like CNNs, hybrid approaches, and novel architectures in achieving high accuracies and improved performance metrics in applications such as disease identification, image analysis, counterfeit detection, and more. While these methods automate the extraction of meaningful features, they also encounter challenges like computational complexity, dataset quality, and real-world variability, which should be carefully considered in their practical implementation.

6 Image classification

Image classification is a fundamental task in computer vision that involves categorizing images into predefined classes or labels. The goal is to enable machines to recognize and differentiate objects, scenes, or patterns within images.

Traditional classification is a fundamental data analysis technique that involves categorizing data points into specific classes or categories based on predetermined rules and established features. Before the advent of deep learning, several conventional methods were widely used for this purpose, including Decision Trees, Support Vector Machines (SVM), Naive Bayes, and k-Nearest Neighbors (k-NN). In the realm of traditional classification, experts would carefully design and select features that encapsulate relevant information from the data. These features are typically chosen based on domain knowledge and insights, aiming to capture distinguishing characteristics that help discriminate between different classes. While effective in various scenarios, traditional classification methods often require manual feature engineering, which can be time-consuming and may not fully capture intricate patterns and relationships present in complex datasets. These selected features act as inputs for classification algorithms, which utilize predefined criteria to assign data points to specific classes. Table 5 provides a compact overview of strengths and limitations in the realm of image classification by examining various deep learning models.

In the realm of medical image analysis, Sarah Ali et al. (Ismael et al. 2020 ) introduced an advanced approach that harnesses the power of Residual Networks (ResNets) for brain tumor classification. Their study involved a comprehensive evaluation on a benchmark dataset comprising 3064 MRI images of three distinct brain tumor types. Impressively, their model achieved a remarkable accuracy of 99%, surpassing previous works in the same domain. Shifting focus to the domain of remote sensing, Xiaowei et al. ( 2020 ) embarked on a deep learning journey for remote sensing image classification. Their methodology combined Recurrent Neural Networks (RNN) with Random Forest, aiming to optimize cross-validation on the UC Merced dataset. Through rigorous experimentation and comparison with various deep learning techniques, their approach achieved a commendable accuracy of 87%.

Texture analysis and classification hold significant implications, as highlighted by Aggarwal and Kuma ( 2020 ). Their study introduced a novel deep learning-based model, centered around Convolution Neural Networks (CNN), specifically composed of two sub-models. The outcomes were noteworthy, with model-1 achieving an accuracy of 92.42%, while model-2 further improved the accuracy to an impressive 96.36%.

Abdar et al. ( 2021 ) unveiled a pioneering hybrid dynamic Bayesian Deep Learning (BDL) model that leveraged the Three-Way Decision (TWD) theory for skin cancer diagnosis. By incorporating different uncertainty quantification (UQ) methods and deep neural networks within distinct classification phases, they attained substantial accuracy and F1-score percentages on two skin cancer datasets.

The landscape of medical diagnostics saw another stride forward with Ibrahim et al. ( 2021 ), who explored a deep learning approach based on a pretrained AlexNet model for classifying COVID-19, pneumonia, and healthy CXR scans. Their model exhibited notable performance in both three-way and four-way classifications, achieving high accuracy, sensitivity, and specificity percentages.

In the realm of image classification under resource constraints, Ma et al. ( 2022 ) introduced a novel deep CNN classification method with knowledge transfer. This method showcased superior performance compared to traditional histogram-based techniques, achieving an impressive classification accuracy of 93.4%.

Diving into agricultural applications, Gill et al. ( 2022 ) devised a hybrid CNN-RNN approach for fruit classification. Their model demonstrated remarkable efficiency and accuracy in classifying fruits, showcasing its potential for aiding in quality assessment and sorting.

Abu-Jamie et al. et al. ( 2022 ) turned their attention to fruit classification as well, utilizing a deep learning-based approach. By employing CNN Model VGG16, they managed to achieve a remarkable 100% accuracy, underscoring the potential of such methodologies in real-world applications.

Medical imaging remained a prominent field of exploration, as Sharma et al. ( 2022 ) explored breast cancer diagnosis through Convolutional Neural Networks (CNN) with transfer learning. Their study showcased a promising accuracy of 98.4%, reinforcing the potential of deep learning in augmenting medical diagnostics.

Beyond the realm of medical imagery, Yang et al. ( 2022 ) applied diverse CNN models to an urban wetland identification framework, with DenseNet121 emerging as the top-performing model. The achieved high Kappa and OA values underscore the significance of deep learning in land cover classification.

Hussain et al. ( 2020 ) delved into Alzheimer's disease detection using a 12-layer CNN model. Their approach showcased a remarkable accuracy of 97.75%, surpassing existing CNN models on the OASIS dataset. Their study also provided a head-to-head comparison with pre-trained CNNs, solidifying the efficacy of their proposed approach in enhancing Alzheimer's disease detection.

In the textile industry, Gao et al. ( 2019 ) addressed fabric defect detection using deep learning. Their novel approach, involving a convolutional neural network with multi-convolution and max-pooling layers, showcased promising results with an overall detection accuracy of 96.52%, offering potential implications for real-world practical applications.

Expanding the horizon to neurological disorders, Vikas et al. study ( 2021 ) pioneered ADHD classification from resting-state functional MRI (rs-fMRI) data. Employing a hybrid 2D CNN–LSTM model, the study achieved remarkable improvements in accuracy, specificity, sensitivity, F1-score, and AUC when compared to existing methods. The integration of deep learning with rs-fMRI holds the promise of a robust model for effective ADHD diagnosis and differentiation from healthy controls.

Skouta et al. ( 2021 ) work focused on retinal image classification. By harnessing the capabilities of convolutional neural networks (CNNs), their approach achieved an impressive classification accuracy of 95.5% for distinguishing between normal and proliferative diabetic retinas. The inclusion of an expanded dataset contributed to capturing intricate features and ensuring accurate classification outcomes. These studies collectively illuminate the transformative influence of deep learning techniques across diverse classification tasks, spanning medical diagnoses, texture analysis, image categorization, and neurological disorder identification.

While traditional methods have their merits, they heavily rely on domain expertise for feature selection and algorithm tuning. However, these traditional classification approaches encounter limitations. They might struggle with complex and high-dimensional data, where identifying important features becomes intricate. Additionally, they demand substantial manual effort in feature engineering, making them less adaptable to evolving data distributions or novel data types. The emergence of deep learning has revolutionized classification by automating the process of feature extraction. Deep neural networks directly learn hierarchical representations from raw data, eliminating the need for manually crafted features. This enables them to capture intricate patterns and relationships that traditional methods might miss. Notably, Convolutional Neural Networks (CNNs) have excelled in image classification tasks, while Recurrent Neural Networks (RNNs) demonstrate proficiency in handling sequential data. These deep learning models often surpass traditional methods in tackling complex tasks across various domains.

7 Discussion

Among the deep learning model for image denoising, Self2Self NN for cost reduction with data augmentation dependency, Denoising CNNs enhancing accuracy but facing resource challenges, and DFT-Net managing image label imbalance while risking detail loss. Robustness and hyperparameter tuning characterize MPR-CNN, while R2R noise reduction balances results and computational demands. CNN architectures prevent overfitting in denoising, and HLF-DIP achieves high values despite complexity. (Noise 2Noise) models exhibit efficiency and generalization trade-offs, and ConvNet enhances receptive fields while grappling with interpretability. This collection offers insights into the evolving landscape of image processing techniques.

This compilation of studies showcases a variety of image enhancement techniques. Ming Liu et al. employ Fuzzy-CNN and F-Capsule for iris recognition, ensuring robustness and avoiding overfitting. Khairul Munadi combines various methods with EfficientNet and ResNets for tuberculosis image enhancement, enhancing generalization while facing time and memory challenges. Ching Ta Lu employs FCNN mean filters for noise reduction, addressing noise while considering potential detail loss. Yuhui Quan implements CV-CNN for image deblurring, providing an efficient model with overfitting prevention. Dan Jin employs pix2pixHD for high-quality MDCT image enhancement, achieving quality improvement with possible overfitting concerns. Guofa Li introduces LE-net for low-light image recovery, emphasizing generalization and robustness with real-world limitations. Xianjie Gao introduces RetinexDIP for image enhancement, offering faster convergence and reduced runtime, despite challenges in complex scenes. Kiyeon Kim unveils MSSNet-WS for single image deblurring, prioritizing computational efficiency in real-world scenarios.

This compilation of research papers presents a comprehensive exploration of deep learning methodologies applied to two prominent types of image segmentation: semantic segmentation and instance segmentation. In the realm of semantic segmentation, studies utilize architectures like FCN, U-Net, and DeepLabV3 for tasks such as efficient detection of multiple persons and robust object recognition in varying lighting and background conditions. These approaches achieve notable performance metrics, with IoU and mIoU ranging from 80 to 86%. Meanwhile, in the context of instance segmentation, methods like Mask-RCNN and AFD-UNet are employed to precisely delineate individual object instances within an image, contributing to efficient real-time waste collection, accurate medical image interpretation, and more. The papers highlight the benefits of these techniques, including enhanced boundary delineation, reduced manual intervention, and substantial time savings, while acknowledging challenges such as computational complexity, model customization, and hardware limitations. This compilation provides a comprehensive understanding of the strengths and challenges of deep learning-based semantic and instance segmentation techniques across diverse application domains.

This review explores deep learning methodologies tailored to different types of image feature extraction across varied application domains. Texture/color-based approaches encompass studies like Aurangzeb Magsi et al.’s disease classification achieving 89.4% ACC, and Weiguo Zhang’s counterfeit detection at 97% accuracy. Pattern-based analysis includes Akey Sungheetha’s 97% class score for retinal images, K. Shankar et al.'s 95.1%-95.7% accuracy using FM-ANN, GLCM, GLRM, and LBP for chest X-rays, and Shahab Ahmad's 99.5% accuracy with AlexNet-GRU for PCam images. Geometric feature extraction is demonstrated by Sharif, Muhammad with 99.4% accuracy in capsule endoscopy images and Aarthi.R et al. achieving 97% accuracy in real-time waste image analysis using MRCNN. This comprehensive review showcases deep learning's adaptability in extracting diverse image features for various applications.

This compilation of research endeavors showcases diverse deep learning models applied to distinct types of image classification tasks. For multiclass classification, studies like Sarah Ali et al.'s employment of Residual Networks attains 99% accuracy in MRI image classification, while Akarsh Aggarwal et al.'s CNN approach achieves 92.42% accuracy in Kylberg Texture datasets. Abdullahi Umar Ibrahim's utilization of an AlexNet model records a 94% accuracy rate for lung conditions. In multiclass scenarios, Harmandeep Singh Gill's hybrid CNN-RNN attains impressive results in fruit classification, and Tanseem N et al. achieve 100% accuracy with VGG16 on fruit datasets. For binary classification, Emtiaz Hussain et al.'s CNN achieves 97.75% accuracy in OASIS MRI data, while Can Gao et al. achieve 96.52% accuracy in defect detection for fabric images. Vikas Khullar et al.'s CNN-LSTM hybrid records 95.32% accuracy for ADHD diagnosis, and Ayoub Skouta's CNN demonstrates 95.5% accuracy in diabetic retinopathy detection. These studies collectively illustrate the efficacy and adaptability of deep learning techniques across various types of classification tasks while acknowledging challenges such as dataset biases, computational intensity, and interpretability.

8 Conclusions

This comprehensive review paper embarks on an extensive exploration across the diverse domains of image denoising, enhancement, segmentation, feature extraction, and classification. By meticulously analyzing and comparing these methodologies, it offers a panoramic view of the contemporary landscape of image processing. In addition to highlighting the unique strengths of each technique, the review shines a spotlight on the challenges that come hand in hand with their implementation.

In the realm of image denoising, the efficacy of methods like Self2Self NN, DnCNNs, and DFT-Net is evident in noise reduction, although challenges such as detail loss and hyperparameter optimization persist. Transitioning to image enhancement, strategies like Novel RetinexDIP, Unsharp Masking, and LE-net excel in enhancing visual quality but face complexities in handling intricate scenes and maintaining image authenticity.

Segmentation techniques span the gamut from foundational models to advanced ones, providing precise object isolation. Yet, challenges arise in scenarios with overlapping objects and the need for robustness. Feature extraction methodologies encompass a range from CNNs to LSTM-augmented CNNs, unveiling crucial image characteristics while requiring careful consideration of factors like efficiency and adaptability.

Within classification, Residual Networks to CNN-LSTM architectures showcase potential for accurate categorization. However, data dependency, computational complexity, and model interpretability remain as challenges. The review's contributions extend to the broader image processing field, providing a nuanced understanding of each methodology's traits and limitations. By offering such insights, it empowers researchers to make informed decisions regarding technique selection for specific applications. As the field evolves, addressing challenges like computation demands and interpretability will be pivotal to fully realize the potential of these methodologies.

The scope of papers discussed in this review offers a panorama of DL methodologies that traverse diverse application domains. These domains encompass medical and satellite imagery, botanical studies featuring flower and fruit images, as well as real-time scenarios. The tailored DL approaches for each domain underscore the adaptability and efficacy of these methods across multifaceted real-world contexts.

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Throughout the 21 st century, the human demand for information has been increasing every day. The choice of an electronic imaging device is related to its application. With the rapid technological developments and use of mobile devices and social media, humans are consistently exposed to a significant amount of information, including digital images and videos. With every minute that passes, the internet is flooded with huge amounts of digital content. Hence, digital imaging has obtained a substantial role in various scientific expeditions, for instance, in image enhancement, restorations, and various object recognition tasks. Often, the colors and contrast of many real life images degrade abruptly due to various factors, such as insufficient lighting, excessive light absorption, scattering, and of course limitations of the imaging devices themselves. Similarly, the hardware restrictions of image or video capturing devices also affects the imaging quality. Moreover, the selective absorption and scattering of light tends to cause color deviations in many real life images, which results in a blurry image and poor contrast. Furthermore, in various situations, digital images are distorted, which sooner or later degrades the visual experience for human viewers. For instance, adverse weather conditions, such as rain, snow, fog, or cloudy environments result in blurry images along with color distortions. Although imaging equipment with better embedded hardware can improve the image quality to a certain extent, in many situations, its adaptability is poor. Hence, the quality of the acquired images is non-satisfactory.

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Top 10 Digital Image Processing Project Topics

We guide research scholars in choosing novel digital image processing project topics. What is meant by digital image processing? Digital Image Processing is a method of handling images to get different insights into the digital image. It has a set of technologies to analyze the image in multiple aspects for better human / machine image interpretation . To be clearer, it is used to improve the actual quality of the image or to abstract the essential features from the entire picture is achieved through digital image processing projects.

This page is about the new upcoming Digital Image Processing Project Topics for scholars who wish to create a masterpiece in their research career!!!

Generally, the digital image is represented in the form of pixels which are arranged in array format. The dimension of the rectangular array gives the size of the image (MxN), where M denotes the column and N denotes the row. Further, x and y coordinates are used to signify the single-pixel position of an image. At the same time, the x value increases from left to right, and the y value increases from top to bottom in the coordinate representation of the image. When you get into the DIP research field, you need to know the following key terminologies.

Important Digital Image Processing Terminologies  

• Stereo Vision and Super Resolution
• Multi-Spectral Remote Sensing and Imaging
• Digital Photography and Imaging
• Acoustic Imaging and Holographic Imaging
• Computer Vision and Graphics
• Image Manipulation and Retrieval
• Quality Enrichment in Volumetric Imaging
• Color Imaging and Bio-Medical Imaging
• Pattern Recognition and Analysis
• Imaging Software Tools, Technologies and Languages
• Image Acquisition and Compression Techniques
• Mathematical Morphological Image Segmentation

Image Processing Algorithms

In general, image processing techniques/methods are used to perform certain actions over the input images, and according to that, the desired information is extracted in it. For that, input is an image, and the result is an improved/expected image associated with their task. It is essential to find that the algorithms for image processing play a crucial role in current real-time applications. Various algorithms are used for various purposes as follows, 

• Digital Image Detection
• Image Reconstruction
• Image Restoration
• Image Enhancement
• Image Quality Estimation
• Spectral Image Estimation
• Image Data Compression

For the above image processing tasks, algorithms are customized for the number of training and testing samples and also can be used for real-time/online processing. Till now, filtering techniques are used for image processing and enhancement, and their main functions are as follows, 

• Brightness Correction
• Contrast Enhancement
• Resolution and Noise Level of Image
• Contouring and Image Sharpening
• Blurring, Edge Detection and Embossing

Some of the commonly used techniques for image processing can be classified into the following, 

• Medium Level Image Processing Techniques – Binarization and Compression
• Higher Level Image Processing Techniques – Image Segmentation
• Low-Level Image Processing Techniques – Noise Elimination and Color Contrast Enhancement
• Recognition and Detection Image Processing Algorithms – Semantic Analysis

Next, let’s see about some of the traditional image processing algorithms for your information. Our research team will guide in handpicking apt solutions for research problems . If there is a need, we are also ready to design own hybrid algorithms and techniques for sorting out complicated model . 

Types of Digital Image Processing Algorithms

• Hough Transform Algorithm
• Canny Edge Detector Algorithm
• Scale-Invariant Feature Transform (SIFT) Algorithm
• Generalized Hough Transform Algorithm
• Speeded Up Robust Features (SURF) Algorithm
• Marr–Hildreth Algorithm
• Connected-component labeling algorithm: Identify and classify the disconnected areas
• Histogram equalization algorithm: Enhance the contrast of image by utilizing the histogram
• Adaptive histogram equalization algorithm: Perform slight alteration in contrast for the  equalization of the histogram
• Error Diffusion Algorithm
• Ordered Dithering Algorithm
• Floyd–Steinberg Dithering Algorithm
• Riemersma Dithering Algorithm
• Richardson–Lucy deconvolution algorithm : It is also known as a deblurring algorithm, which removes the misrepresentation of the image to recover the original image
• Seam carving algorithm : Differentiate the edge based on the image background information and also known as content-aware image resizing algorithm
• Region Growing Algorithm
• GrowCut Algorithm
• Watershed Transformation Algorithm
• Random Walker Algorithm
• Elser difference-map algorithm: It is a search based algorithm primarily used for X-Ray diffraction microscopy to solve the general constraint satisfaction problems
• Blind deconvolution algorithm : It is similar to Richardson–Lucy deconvolution to reconstruct the sharp point of blur image. In other words, it’s the process of deblurring the image.

Nowadays, various industries are also utilizing digital image processing by developing customizing procedures to satisfy their requirements. It may be achieved either from scratch or hybrid algorithmic functions . As a result, it is clear that image processing is revolutionary developed in many information technology sectors and applications.  

Digital Image Processing Techniques

• In order to smooth the image, substitutes neighbor median / common value in the place of the actual pixel value. Whereas it is performed in the case of weak edge sharpness and blur image effect.
• Eliminate the distortion in an image by scaling, wrapping, translation, and rotation process
• Differentiate the in-depth image content to figure out the original hidden data or to convert the color image into a gray-scale image
• Breaking up of image into multiple forms based on certain constraints. For instance: foreground, background
• Enhance the image display through pixel-based threshold operation 
• Reduce the noise in an image by the average of diverse quality multiple images 
• Sharpening the image by improving the pixel value in the edge
• Extract the specific feature for removal of noise in an image
• Perform arithmetic operations (add, sub, divide and multiply) to identify the variation in between the images 

Beyond this, this field will give you numerous Digital Image Processing Project Topics for current and upcoming scholars . Below, we have mentioned some research ideas that help you to classify analysis, represent and display the images or particular characteristics of an image.

Latest 11 Interesting Digital Image Processing Project Topics

• Acoustic and Color Image Processing
• Digital Video and Signal Processing
• Multi-spectral and Laser Polarimetric Imaging
• Image Processing and Sensing Techniques
• Super-resolution Imaging and Applications
• Passive and Active Remote Sensing
• Time-Frequency Signal Processing and Analysis
• 3-D Surface Reconstruction using Remote Sensed Image
• Digital Image based Steganalysis and Steganography
• Radar Image Processing for Remote Sensing Applications
• Adaptive Clustering Algorithms for Image processing

Moreover, if you want to know more about Digital Image Processing Project Topics for your research, then communicate with our team. We will give detailed information on current trends, future developments, and real-time challenges in the research grounds of Digital Image Processing.

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Research Topics

Biomedical Imaging

The current plethora of imaging technologies such as magnetic resonance imaging (MR), computed tomography (CT), position emission tomography (PET), optical coherence tomography (OCT), and ultrasound provide great insight into the different anatomical and functional processes of the human body.

Computer Vision

Computer vision is the science and technology of teaching a computer to interpret images and video as well as a typical human. Technically, computer vision encompasses the fields of image/video processing, pattern recognition, biological vision, artificial intelligence, augmented reality, mathematical modeling, statistics, probability, optimization, 2D sensors, and photography.

Image Segmentation/Classification

Extracting information from a digital image often depends on first identifying desired objects or breaking down the image into homogenous regions (a process called 'segmentation') and then assigning these objects to particular classes (a process called 'classification'). This is a fundamental part of computer vision, combining image processing and pattern recognition techniques.

Multiresolution   Techniques

The VIP lab has a particularly extensive history with multiresolution methods, and a significant number of research students have explored this theme. Multiresolution methods are very broad, essentially meaning than an image or video is modeled, represented, or features extracted on more than one scale, somehow allowing both local and non-local phenomena.

Remote Sensing

Remote sensing, or the science of capturing data of the earth from airplanes or satellites, enables regular monitoring of land, ocean, and atmosphere expanses, representing data that cannot be captured using any other means. A vast amount of information is generated by remote sensing platforms and there is an obvious need to analyze the data accurately and efficiently.

Scientific Imaging

Scientific Imaging refers to working on two- or three-dimensional imagery taken for a scientific purpose, in most cases acquired either through a microscope or remotely-sensed images taken at a distance.

Stochastic Models

In many image processing, computer vision, and pattern recognition applications, there is often a large degree of uncertainty associated with factors such as the appearance of the underlying scene within the acquired data, the location and trajectory of the object of interest, the physical appearance (e.g., size, shape, color, etc.) of the objects being detected, etc.

Video Analysis

Video analysis is a field within  computer vision  that involves the automatic interpretation of digital video using computer algorithms. Although humans are readily able to interpret digital video, developing algorithms for the computer to perform the same task has been highly evasive and is now an active research field.

Evolutionary Deep Intelligence

Deep learning has shown considerable promise in recent years, producing tremendous results and significantly improving the accuracy of a variety of challenging problems when compared to other machine learning methods.

Discovery Radiomics

Radiomics, which involves the high-throughput extraction and analysis of a large amount of quantitative features from medical imaging data to characterize tumor phenotype in a quantitative manner, is ushering in a new era of imaging-driven quantitative personalized cancer decision support and management. 

Sports Analytics

Sports Analytics is a growing field in computer vision that analyzes visual cues from images to provide statistical data on players, teams, and games. Want to know how a player's technique improves the quality of the team? Can a team, based on their defensive position, increase their chances to the finals? These are a few out of a plethora of questions that are answered in sports analytics.

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What is Image Processing? Examples, Types, and Benefits

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Analog versus digital image processing

Types of image processing, how are digital images processed, how image processing is used in the real world, benefits of image processing, how has ai changed and enhanced image processing, top 5 image recognition software.

We see thousands of images every day, online and out in the real world. It’s likely that the images have been changed in some way before being released into the wild. 

Whether someone simply brightened or sharpened the visuals or performed more extensive edits to extract critical information, many industries rely on the technique of image processing to complete their work.

What is image processing?

Image processing is a group of methods used to understand, interpret, and alter visual data. Two-dimensional digital images are made up of pixels, a small unit that contains bits of information about the shade, color, and opacity of that specific part of the visual. Together, all of the pixels make up the full image. This data is then processed to enhance the image or to extract information contained within it.

While image processing has been around for at least 80 years in some form, technological developments over the last decade have seen an increase in the use of artificial intelligence (AI) tools. Algorithms have been developed to replicate how the human brain would process these images. Image recognition software , also known as computer vision, completes the processing functions that the machine has been trained to perform. 

Most forms of image processing these days are digital, which sees pixelated graphics processed through a computer using an algorithm. With AI, these algorithms elevate the precision and sophistication of identification and modification.

Analog image processing still happens, though. Special types of optical computers are used to process physical images using light waves generated by the object. Hard copying, like printing or photocopying, stands as the most common application of analog image processing.

Want to learn more about Image Recognition Software? Explore Image Recognition products.

The goal for most image processing is to either improve the quality of the visual itself or to gain a better understanding of different elements in the image. Different objectives call for different types of processing. 

Some of the most common types of image processing are:

• Image enhancement. Not every picture comes out perfectly in its original form. Image processing tools can alter the quality of images by doing things like adjusting the brightness, sharpness, clarity, and contrast. 
• Object detection and classification. The practice of object detection identifies different elements within an image. You can find patterns when they’re cleanly separated in a visual or you can quickly highlight specific objects when the visual is scanned.
• Image segmentation . Images may need to be divided into different sections for object detection or other purposes. After that, you can analyze the separate regions independently from each other. This happens a lot in medical imaging like MRIs, which shows different shades of gray and black to represent solid masses around fluid.
• Image compression. This type reduces the file size of an image while still preserving its original quality. Compression makes uploading images to websites faster, improves page loading times, and minimizes storage needs for businesses that keep numerous image files.
• Image restoration. Images of any kind can lose their quality over time. Physical photos especially degrade over decades and iImage processing is a good way to restore the original look and feel, especially for physical photographs.

What is annotation in image processing?

The practice of image annotation labels elements within digital images. This refers to when it’s done manually by humans or digitally by computers. It lets computers interpret an image and extract important information.

When AI functions as the primary method of image processing, machine learning (ML) engineers typically predetermine the labels entered into a digital image processing algorithm, helping introduce the computer to different objects.

This is an essential part of the object detection and classification process, as any mistakes here become difficult to fix as the machine learning tool grows. Precision and accuracy at this early stage of training are non negotiable.

For any image processing project, there are several key steps that must happen for the image to be thoroughly altered (if necessary) and reviewed before a better output can be generated. Not every image will need to go through all of these steps, but this sequence is the most commonly used in image processing.

1. Acquisition 

The first simple step is taking a photo on a camera or converting an analog image to a digital one. Also known as pre-processing, acquiring the image moves the image from its original source and uploads it to a computer.

2. Enhancement or restoration

Edits to the image can start right away. This could include sharpening the image to remove blurry features, increasing the contrast to better see different parts of the image, or restoring areas of the image that may have been damaged.

3. Color processing

When color visuals, you might need corrections at this stage to match the final colors of the image as accurately as possible to a standardized color chart.

4. Wavelets and multi-resolution processing

Wavelets represent different parts of the image at various resolution levels. When an image is divided into its wavelets for compression and analysis, the computer has an easier time working on a smaller scale.

5. Compression

Reducing the size of the image at this point in the process scales down the file size and simultaneously keeps the image quality as high as possible.

6. Morphological processing

Different elements of the image may be morphed together during processing if they’re not needed for analysis or extraction. This reduces overall processing times.

7. Segmentation

At this important step, each region of the graphic is broken down into groups based on characteristics in the pixels. This helps discern different areas of the image.

8. Representation and description

This step helps find borders in segmented regions of the image. Attributes of these segmented regions are assigned during the description phase, which distinguishes one group from another.

9. Object detection

Once all of the image segments have been described and assigned, labels are added to let human users identify the different parts of the image. For instance, in a street scene, object detection differentiates between cars and street lamps and then labels them accordingly.

Hundreds of applications for image processing exist – from healthcare and agriculture to security and legal services.

of all business-related tasks are performed by machines.

Source: World Economic Forum

Face and text recognition

Facial recognition software looks for comparisons between two images, usually between a person, or a live image of the person, and an ID, like a passport or driver’s license. This software can also be used for multi-factor authentication (MFA) for unlocking a phone, along with automatic tagging in photos on social media platforms.

This technology doesn’t just help with images. You can also turn to these tools to scan for recognizable patterns, both in type- and handwritten text. The documents can also be entered into natural language processing (NLP) software for extraction, annotation, and review, just like with visuals.

Reverse image search

Have you ever done a reverse Google Images search? That’s powered by image processing technology. Reverse image searches assess the features in the original image and scan the web for similar or exact matches of that image elsewhere online.

Autonomous vehicle object detection

Self-driving vehicles must immediately and constantly sense possible hazards like pedestrians, buildings, and other cars to keep everyone safe from them. Object detection algorithms can quickly identify specific objects within the vehicle’s viewing radius, which triggers the car’s safety functions.

Medical imaging

From research to diagnosis to recovery, medical professionals apply image processing technology extensively. Healthcare workers detect tumors and other anomalies while 3D image processing empowers surgeons to navigate the most complex parts of our anatomy.  

Professionals across fields have found many benefits from using image processing tools. Just a few are mentioned here.

Increased accuracy

Image processing tools detect even the smallest detail, which makes finding errors much easier. Automating many of the steps in the image processing pipeline reduces human error. Many industries, like medicine and agriculture, put a lot of trust in the high level of precision that modern image processing offers.

Cost savings

Catching issues early in the process, like in product manufacturing or retail, means that businesses save money on correcting these later with recalls or returns. Image processing can be used for quality control to identify possible defects in products as they’re made, along with verifying information such as batch numbers or expiration dates. If errors are made during manufacturing but are spotted straight away, they can be fixed before going out to customers.

Real-time updates

When image processing tools are used in industries like security and surveillance, their ability to communicate real-time data can mark the difference between a criminal’s success or failure. This allows security teams to act quickly when responding to incidents.

Improved customer experience

Customer-facing fields, such as retail and hospitality, use image processing in a number of ways. This includes comparing a digital capture of inventory in a stockroom or warehouse against system inventory levels. 

This ensures that stock counts are accurate and gives managers the okay to reorder. Now, customers don’t have to wait as long for their items.

The introduction of AI to image processing has significantly changed the way many industries use this technology in their day-to-day. As algorithms become more sophisticated at training machines to think and process like humans, the applications for this technology continue to grow.

Using deep learning with image processing has cleared the path for computers to detect objects within an image and recognize patterns more accurately. The models we have today process and understand visual data much faster than traditional digital or analog image processing techniques. 

For many of the industries that already count on image processing, AI has improved efficiency by automating even the most complex tasks like segmentation and image enhancement.

Facial and object recognition exists as one of the most used applications of AI image processing. Image generation also takes up space in this field by creating new work based on information from previously created visuals. 

The process of digital image processing using AI

Engineers use ML techniques to harness the power of AI algorithms for interpreting visual data. Neural networks, The core functionality behind this process consists of neural networks, interconnected nodes placed together in a layered structure to mimic the way a human brain understands data. After they’re in position, the algorithm can conduct its image processing, using the following method.

• Data collection. The first stage is gathering a large dataset of labeled or annotated images to train the algorithm on. They should relate closely to your project or task; more relevant data upfront increases the odds of accurate outcomes later. At this stage, images are processed to resize them for consistency.
• Pattern recognition. Ahead of training, the model begins to identify and distinguish patterns within the dataset.
• Model training . Here, the neural network starts reviewing the input dataset and all elements within it, like image labels or patterns. This information will help develop the neural network’s intelligence for use in future projects.
• Feature extraction. Trained models should reach a point where they can start doing work on their own, including identifying the features of new, previously unseen images. Based on what the algorithm learned during the train phase, relevant features should now be recognizable. For instance, in facial recognition , neural networks should be able to pinpoint facial features like noses or eyes at this stage.
• Validation. Think of this as the testing stage for all of the completed steps. You compare a separate validation dataset to the model’s performance so far to find inaccuracies and areas that need fine-tuning. 
• Inference. At this point, you introduce new images to the model for continuing training once errors have been corrected. This builds on the previously-learned patterns and allows the model to start making its own predictions about new visuals
• Learning and improvement. The process continues even after fully-trained models have been deployed. Continual improvement through additional cycles of training with new data improves performance and raises accuracy over time.

Image processors or recognition tools are used by data scientists to train image recognition models and to help engineers adapt existing software to have image processing capabilities. These software are an important part of machine learning and enable businesses to do more with their visual media.

To be included in the image recognition software category, platforms must:

• Provide a deep learning algorithm specifically for image recognition 
• Connect with image data pools to learn a specific solution or function 
• Consume the image data as an input and provide an outputted solution 
• Provide image recognition capabilities to other applications, processes, or services

* Below are the top five leading image recognition software solutions from G2’s Summer 2024 Grid Report. Some reviews may be edited for clarity.

1. Google Cloud Vision API

Google Cloud’s Vision API is an image processing tool that can detect and classify multiple objects within images and helps developers leverage the power of machine learning. With pre-trained ML models, developers are able to classify images into millions of predefined categories for more efficient image processing.

What users like best:

“The best thing about the API is it is trained on a very huge dataset which makes the lives of developers easy as we can build great image recognition models with a very high accuracy without even having big data available with us.”

- Google Cloud Vision API Review , Saurabh D .

What users dislike:

“For low quality images, it sometimes gives the wrong answer as some food has the same color. It does not provide us the option to customize or train the model for our specific use case.”

- Google Cloud Vision API Review , Badal O .

2. Gesture Recognition Toolkit

With the Gesture Recognition Toolkit , developers can use existing datasets to complete real-time image processing quickly and easily. The toolkit is cross platform and open source, making it easy for both new and experienced developers to benefit from others working on similar projects.

“I like how it is designed to work with real time sensor data and at the same time the traditional offline machine learning task. I like that it has a double precision float and can easily be changed to single precision, making it a very flexible tool.”

- Gesture Recognition Toolkit Review , Diana Grace Q .

“Gesture Recognition Toolkit has occasional lag and a less smooth implementation process.”

- Gesture Recognition Toolkit Review , Civic V .

3. SuperAnnotate

SuperAnnotate is a leading image annotation software, helping businesses to build, fine-tune, and iterate AI models with high-quality training data. The advanced annotation technology, data curation, automated features, and data governance tools enable you to build large scale AI models with predetermined datasets.

“The platform is very easy and intuitive to use. The user interface is friendly and everything is easy to find.”

- SuperAnnotate Review , Dani S .

“We have had some issues with custom workflows that the team implemented for specific projects on their platform.”

- SuperAnnotate Review , Rohan K .

Syte is a visual AI product discovery platform that uses camera search, personalization engine, and in-store tools to help eCommerce and brick-and-mortar retail businesses connect shoppers with their products. The tools are instant and intuitive, making it easy for shoppers to discover and purchase products.

“The visual search discovery button is a great addition to our ecommerce site. I like that it helps customers find similar items visually for products that might not be in their size, thereby increasing conversion and the overall shopping experience. I also like that customers can adjust the visual search selection to encourage cross-shopping with other items featured in our images.”

- Syte Review , Lexis K .

“The backend merch platform is not the most intuitive as other platforms. The “complete the look” function doesn't showcase the exact products part of the look, only lookalikes.”

- Syte Review , Cristina F .

5. Dataloop

Dataloop allows developers to build custom algorithms and train data throughout all parts of the AI lifecycle. From management and annotation to model selection and deployment, Dataloop uses intuitive features to help you get the most out of your AI systems.

“DataLoop excels at constructing quality data infrastructure for unstructured data, streamlining computer-vision pipelines, and ensuring seamless integration with robust security measures.”

- Dataloop Review , George M .

“I have had challenges with some steep learning curves, infrastructure dependency, and customization limitations. These have in a way limited me in its usage.”

- Dataloop Review , Dennis R .

Picture this: perfect pixels every time!

Using AI to label, classify, and process your image can save your team time every month. Train your machine with the right functions and datasets so it becomes a customized worker that improves performance with accuracy and efficiency. 

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Holly Landis

Holly Landis is a freelance writer for G2. She also specializes in being a digital marketing consultant, focusing in on-page SEO, copy, and content writing. She works with SMEs and creative businesses that want to be more intentional with their digital strategies and grow organically on channels they own. As a Brit now living in the USA, you'll usually find her drinking copious amounts of tea in her cherished Anne Boleyn mug while watching endless reruns of Parks and Rec.

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Research in Medical Imaging Using Image Processing Techniques

• In book: Medical Imaging - Principles and Applications [Working Title]

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