Image Processing Tasks Research Articles

Comparison of different algorithms to improve the quality of Image

Super-resolution (SR) become the most important task in image processing because of requirement of high quality of images. It becomes popular in technology like computer vision and pattern recognition. In Single Image Super-resolution (SR) uses the sequences of single low resolution (LR) images from high resolution (HR) to improve the quality of image. The SR is an image processing problem which aims to generate a high resolution (HR) image from low resolution input image. The SR is difficult because of the missing information in the given LR image. Thus in SR it is very important to boost the reconstruction performance so that, there is need of effective prior knowledge. The prior knowledge of image will solve the problem to improve the quality of generated image. The main aim of SR is to generate the HR image with good visual perception as similar as original image. The paper compare two methods of SR that is bi-linear method, and direction group sparsity of image method that is proposed one. These methods will evaluate the accuracy, subjective visual effect, quality of image, time complexity and PSNR, etc. These calculations will enhance nature of picture like better visual impact quality, lower reconstruction error and adequate calculation proficiency, and so on

KUMPAS: A Filipino Sign Language Translator of Deaf and Hearing Individuals Utilizing Computer Vision Algorithm

This study elucidates how the Filipino “Kumpas” translator improves communication between signers and non-signers. Many Hearing people are likely familiar with American Sign Language (ASL), but many are still unaware that the Filipino Deaf have their own unique language called Filipino Sign Language (FSL). In this section, the programmers present a method for bridging the gap between signers and non-signers. Sign to text and speech to sign are the two modes of communication for the “Kumpas” translator. In order to translate using speech, the application would use a speech audio input and based from the words recognized, a 3D character will play a sequence of animations according to the translation. The OpenCV Python library, which is useful for computer vision and image processing tasks, was used by the programmers. MediaPipe has a number of features, including tracking, face recognition, and object detection. The system was tested using an ISO 25010 and based on the evaluation, an overall very satisfactory rating was obtained. Also, findings presented that the system is definitely a useful language translator for Filipinos.

Computer Vision Algorithm Design in Image Processing Based on Projective Geometry

Image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM's performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain.

Flare Removal Model Based on Sparse-UFormer Networks.

When a camera lens is directly faced with a strong light source, image flare commonly occurs, significantly reducing the clarity and texture of the photo and interfering with image processing tasks that rely on visual sensors, such as image segmentation and feature extraction. A novel flare removal network, the Sparse-UFormer neural network, has been developed. The network integrates two core components onto the UFormer architecture: the mixed-scale feed-forward network (MSFN) and top-k sparse attention (TKSA), creating the sparse-transformer module. The MSFN module captures rich multi-scale information, enabling the more effective addressing of flare interference in images. The TKSA module, designed with a sparsity strategy, focuses on key features within the image, thereby significantly enhancing the precision and efficiency of flare removal. Furthermore, in the design of the loss function, besides the conventional flare, background, and reconstruction losses, a structural similarity index loss has been incorporated to ensure the preservation of image details and structure while removing the flare. Ensuring the minimal loss of image information is a fundamental premise for effective image restoration. The proposed method has been demonstrated to achieve state-of-the-art performance on the Flare7K++ test dataset and in challenging real-world scenarios, proving its effectiveness in removing flare artefacts from images.

Towards a Flexible Semantic Guided Model for Single Image Enhancement and Restoration.

Low-light image enhancement (LLIE) investigates how to improve the brightness of an image captured in illumination-insufficient environments. The majority of existing methods enhance low-light images in a global and uniform manner, without taking into account the semantic information of different regions. Consequently, a network may easily deviate from the original color of local regions. To address this issue, we propose a semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that adaptively integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in the LLIE task. We further present a refined framework SKF++ with two new techniques: (a) Extra convolutional branch for intra-class illumination and color recovery through extracting local information and (b) Equalization-based histogram transformation for contrast enhancement and high dynamic range adjustment. Extensive experiments on various benchmarks of LLIE task and other image processing tasks show that models equipped with the SKF/SKF++ significantly outperform the baselines and our SKF/SKF++ generalizes to different models and scenes well. Besides, the potential benefits of our method in face detection and semantic segmentation in low-light conditions are discussed. The code and pre-trained models have been publicly available at https://github.com/langmanbusi/Semantic-Aware-Low-Light-Image-Enhancement.

Segmentation and visualization of Retinal Detachment lesions through Retinal fundus images

Medical image segmentation is a prominent task in medical image processing and a fundamental aspect of medical image analysis. However, accurate segmentation is exceedingly challenging due to the variations in size, shape, and location of the lesions. U-Net is well-linked in the domain of medical image segmentation, but it fails to fully exploit the advantageous characteristics of the channel or leverage contextual information. This article presents a novel, improved MultiResU-Net framework called DCA-MultiResU-Net to minimize the loss of crucial features and also to enhance the efficiency of end-to-end image segmentation by utilizing the full advantages of U-Net, Res2Net, Channel attention mechanism, Dilated convolution layers, and MultiResUNet models. In contrast to the typical convolutions of U-Net in the encoder path, the dilated convolution modules are inserted into the MultiRes blocks to extract and concatenate the multi-scale features. Furthermore, a channel attention mechanism is employed in the bottom portion of the “U” shaped network and the decoder path to fuse the information from receptive fields of varying sizes. Afterward, the virtual reality visualization technology was implemented for the 3-D visualization of segmented RD lesions. The proposed segmentation model was evaluated on four online databases: Retinal Image Bank, RIADD, Kaggle, and Cataract Image Dataset or GitHub. The performance outcomes of this work were measured using standard evaluation indexes such as Dice similarity coefficient, Intersection over the union, F1-score, and Loss function score of 90.76%, 88.42%, 93.28%, and 0.1641, respectively. The experimental findings indicate that the DA-MultiResU-Net framework achieved superior performance and generalization capabilities while utilizing 6.76% fewer parameters than the U-Net model.

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.

Color image restoration by filtering methods: a review

Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks.

Integrating OpenCV and Pandas for Enhanced Image Filtering and Color Detection

Image filtering and color detection in OpenCV is performed using functions like cv2.filter2D() for filtering, and cv2.cvtColor() for color space conversion and detection. These functions can be combined with other image processing techniques such as thresholding, erosion and dilation, and edge detection to perform more advanced tasks. The process typically starts with loading an image into Python using OpenCV's imread() function. From there, the image may be converted to a different color space using cvtColor() to make it easier to detect certain colors or features. Various image filters can then be applied to enhance the image and reduce noise or other unwanted features. This can include techniques such as blurring, sharpening, or edge detection, depending on the desired outcome. Once the image has been filtered, color detection can be performed using thresholding, which involves setting a threshold value and creating a binary image based on whether each pixel falls above or below the threshold. This can help to identify specific colors or features in the picture. Pandas are particularly useful for working with image data as it provides a flexible and efficient data structure called a DataFrame. This makes it easy to perform data manipulation tasks such as selecting, filtering, and grouping data, which can be useful for image processing tasks. Meanwhile, OpenCV provides a comprehensive set of tools for image filtering and color detection. These tools include a variety of filters, such as Gaussian, Median, and Bilateral filters, as well as thresholding techniques, such as binary and adaptive thresholding, which can be used to detect specific colors or features in an image. Keywords:- Image Processing, OpenCV, Color Detection, Image Filtering, cv2.filter2D, cv2.cvtColor, Thresholding, Erosion and Dilation, Edge Detection, Pandas DataFrame, Gaussian Filter, Median Filter, Bilateral Filter, Adaptive Thresholding, Feature Detection..

Dominant Color Detection For Online Fashion Retrievals

This paper introduces a novel approach aimed at efficiently extracting dominant colors from online fashion images. The method addresses challenges related to detecting overlapping objects and computationally expensive methods by combining K-means clustering and graph-cut techniques into a framework. This framework incorporates an adaptive weighting strategy to enhance color extraction accuracy. Additionally, it introduces a two-phase fashion apparel detection method called YOLOv4, which utilizes U-Net architecture for clothing segmentation to precisely separate clothing items from the background or other elements. Experimental results show that K-means with YOLOv4 outperforms K-means with the U-Net model. These findings suggest that the U-Net architecture and YOLOv4 models can be effective methods for complex image segmentation tasks in online fashion retrieval and image processing, particularly in the rapidly evolving e-commerce environment.

Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task.

An Underwater Image Enhancement Method Based on Diffusion Model Using Dual-Layer Attention Mechanism

Diffusion models have been increasingly utilized in various image-processing tasks, such as segmentation, denoising, and enhancement. These models also show exceptional performance in enhancing underwater images. However, conventional models for underwater image enhancement often face the challenge of simultaneously improving color restoration and super-resolution. This paper introduces a dual-layer attention mechanism that integrates spatial and channel attention to enhance color restoration, while preserving critical image features. Additionally, specific scale factors and interpolation methods are employed during the upsampling process to increase resolution. The proposed DL-UW method achieves significant enhancements in color, illumination, and resolution for low-quality underwater images, resulting in high PSNR, SSIM, and UIQM values. The model demonstrates a robust performance on different datasets, confirming its broad applicability and effectiveness.

Efficient Wheat Head Segmentation with Minimal Annotation: A Generative Approach.

Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the absence of an annotated dataset, synthetic data can be used for model development; however, due to the substantial differences between simulated and real data, a phenomenon referred to as domain gap, the resulting models often underperform when applied to real data. In this research, we aim to address this challenge by first computationally simulating a large-scale annotated dataset and then using a generative adversarial network (GAN) to fill the gap between simulated and real images. This approach results in a synthetic dataset that can be effectively utilized to train a deep-learning model. Using this approach, we developed a realistic annotated synthetic dataset for wheat head segmentation. This dataset was then used to develop a deep-learning model for semantic segmentation. The resulting model achieved a Dice score of 83.4% on an internal dataset and Dice scores of 79.6% and 83.6% on two external datasets from the Global Wheat Head Detection datasets. While we proposed this approach in the context of wheat head segmentation, it can be generalized to other crop types or, more broadly, to images with dense, repeated patterns such as those found in cellular imagery.

Deep learning and satellite remote sensing for biodiversity monitoring and conservation

AbstractIn the context of the current nature crisis, being able to reliably and cost‐effectively track subtle changes in the biosphere across adequate spatial and temporal extents and resolutions is crucial. Deep learning represents a group of versatile approaches to image processing tasks that are increasingly combined with satellite remote sensing imagery to monitor biodiversity and inform ecology and conservation, yet an overview of the opportunities and challenges associated with this development has so far been lacking. Here, we provide an interdisciplinary perspective on current research and technological developments associated with satellite remote sensing and deep learning that have the potential to make a difference in biodiversity monitoring and wildlife conservation; highlight challenges to the broader adoption of these approaches by experts operating at the interface between satellite remote sensing and ecology and conservation; and discuss how these can be overcome. By enabling the leveraging of big data and by providing new ways to learn about biodiversity and its dynamics, deep learning approaches promise to become a powerful tool to help address current monitoring needs and knowledge gaps. In certain situations, deep learning approaches may moreover substantially reduce the time and resources required to analyse satellite imagery. However, issues relating to capacity building, reference data access, environmental costs as well as model interpretability, robustness and alignment need to be addressed to successfully capitalize on these opportunities.

The Impact of Chinese CEOs' Facial Aesthetics on Their Compensation

Much of the psychological and sociological literature has shown that beauty plays a role in the workplace. Does this apply to CEOs and affect their compensation? This study uses the term facial aesthetics to measure CEO beauty. Facial attractiveness and facial maturity are two important components of facial aesthetics. This paper summarizes three methods. The first method uses the ResNet50 deep convolutional neural network, using ImageNet data, which is applied to a variety of image processing tasks. The second method is Three-Dimensional Stereo-Photogrammetry using in major contemporary studies of facial soft-tissue attractiveness. The third approach is the Visual Analogue Scales, the validity of which is also well supported by empirical data, including in terms of test-retest and interrater reliability. The results show that both facial attractiveness and facial maturity have an impact on CEO compensation. Black CEOs with baby faces have higher pay and status than those with more mature faces. CEOs who are more physically attractive are paid more than their less attractive counterparts. However, there is little research on this aspect at present. Like facial maturity, only black CEOs and their pay have been studied, but for the rest of the population, the results are unknown. And, due to the cultural differences, each race also has aesthetic differences.

Onward and Autonomously: Expanding the Horizon of Image Segmentation for Self-Driving Cars through Machine Learning

Autonomous navigation is the leading technology in current era, in this intelligent traffic light, sign detection, ADAS and obstacle detections were playing major role. Image segmentation is the process of dividing an image into different regions, or semantic classes. This is a challenging problem in autonomous vehicle technology because it requires the vehicle to be able to understand its surroundings to safely navigate. The major challenges in this platform are the accuracy and efficiency of model performance. The proposed method in the abstract uses a convolutional neural network (CNN) to perform image segmentation. CNNs are a type of deep learning model that is well-suited for image processing tasks. The CNN in this paper was trained on a local city dataset, and it was able to achieve a mean intersection over union (IoU) of 73%. IoU is a measure of how well the segmentation results match the ground truth labels. A score of 100% indicates that the segmentation is perfect, while a score of 0% indicates that the segmentation is completely wrong. This means that the method can segment images at a very fast rate, which is important for autonomous vehicles that need to make real-time decisions. Overall, the proposed method is a promising approach for image segmentation in autonomous vehicles. It can achieve high accuracy and speed, and it is easy to implement using Python. The proposed method attains an accuracy of 98.34 %, a Sensitivity of 97.26 % and a sensitivity of 96.37 % had been attained. The method could be used to improve the safety and efficiency of autonomous vehicles by enabling them to better understand their surroundings.

A novel Laplacian vector directional filter designed for real-time removal of impulsive noise from color images

Eliminating noise from a color image represents an essential task in image processing, given that noise can markedly diminish visual quality and impact the precision of subsequent analyses or applications (such as visual quality inspection, medical image analysis, etc.). In this study, we introduce an innovative approach designed to remove noise while preserving the edges and fine details of a color image. This method is based on Laplacian operators, threshold values for identifying potentially corrupted pixels due to noise, and the Vector Directional Filter (VDF) for substituting noisy pixels. Consequently, this technique is termed Laplacian VDF (LVDF). Additionally, a Hardware/Software (HW/SW) design is formulated for the real-time implementation of the LVDF filter. The hardware architecture is designed through the High-Level Synthesis (HLS) flow, while the software component is executed using the ARM Cortex-A53 hardcore processor. Furthermore, five Direct Memory Access (DMAs) are employed to enhance data throughput between the hardware coprocessor and Double Data Rate (DDR) memory. Performance evaluations conducted on the ZCU102 kit reveal that the LVDF HW/SW design facilitates the restoration of 74 images per second, resulting in a 94 % reduction in execution time compared to the software implementation, all while maintaining consistent objective and subjective image quality.

Anatomically plausible segmentations: Explicitly preserving topology through prior deformations

Since the rise of deep learning, new medical segmentation methods have rapidly been proposed with extremely promising results, often reporting marginal improvements on the previous state-of-the-art (SOTA) method. However, on visual inspection errors are often revealed, such as topological mistakes (e.g. holes or folds), that are not detected using traditional evaluation metrics. Incorrect topology can often lead to errors in clinically required downstream image processing tasks. Therefore, there is a need for new methods to focus on ensuring segmentations are topologically correct. In this work, we present TEDS-Net: a segmentation network that preserves anatomical topology whilst maintaining segmentation performance that is competitive with SOTA baselines. Further, we show how current SOTA segmentation methods can introduce problematic topological errors. TEDS-Net achieves anatomically plausible segmentation by using learnt topology-preserving fields to deform a prior. Traditionally, topology-preserving fields are described in the continuous domain and begin to break down when working in the discrete domain. Here, we introduce additional modifications that more strictly enforce topology preservation. We illustrate our method on an open-source medical heart dataset, performing both single and multi-structure segmentation, and show that the generated fields contain no folding voxels, which corresponds to full topology preservation on individual structures whilst vastly outperforming the other baselines on overall scene topology. The code is available at: https://github.com/mwyburd/TEDS-Net.

Lipschitz energy functional for anisotropic diffusion applications

Image denoising remains a key research problem because of its potential role as a pre-processing component in image processing, computer vision, and machine learning tasks. Of the available approaches for image denoising, those inspired by anisotropic diffusion processes have been a center of discussion for decades. Despite the efforts and promising results achieved by diffusion-inspired denoising methods, we noted insufficient attention on the design of energy functionals for anisotropic diffusion equations. Most researchers consider heuristic approaches to design diffusivity functionals, a practice that cannot provide mathematical explanations on why their approaches work. The current research presents a strictly convex and Lipschitz energy functional that guarantees a unique solution for an evolutionary process. Based on this functional, we derive an anisotropic diffusion equation for image denoising applications. Experimental results show that an algorithm corresponding to the proposed equation is computationally efficient, and generates informative and visually appealing images with competitive values of peak signal-to-noise ratio and structural similarity. Guided by the compelling properties of our energy functional, we provide an additional insight to describe quality of the results. Implementation codes and test datasets of the proposed approach are publicly accessible at the MATLAB File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/160108-lipschitz-diffusion-inspired-energy-functional).

Augmented Optoelectronic Quantum Dot‐Enhanced Heterogeneous IGZO‐Te Photodiode for Artificial Synaptic Image Processing Applications

AbstractThis study presents a novel photodiode technology with augmented optoelectronic capabilities through the incorporation of quantum dots (QDs). The photodiode, composed of Indium Gallium Zinc Oxide‐Tellurium (IGZO‐Te), is engineered for enhanced light sensitivity and tailored optoelectronic interactions. The primary application of this advanced photodiode lies in the domain of artificial intelligence and machine learning, where it plays a pivotal role in simulating synaptic connections for image processing tasks. Due to the exceptional optical and electrical properties of the IGZO‐Te‐QDs active material combination, the photodiode demonstrates consistent and dependable optical synaptic functions linked to learning. This is achieved through conduction modulation and time duration modulation of light stimulus. In addition, the opto‐synaptic functionalities such as short‐term and long‐term memory has also been successfully demonstrated not only on single device but also on 6 × 6 photodiodes array device. These findings underscore the significant potential of the hybrid structure of IGZO‐Te with QDs in optical artificial neuromorphic chips.