Color Restoration Research Articles

RUDIE: Robust approach for underwater digital image enhancement

Processing underwater digital images is critical in ocean engineering, biology, and environmental studies, focusing on challenges such as poor lighting, image de-scattering, and color restoration. Due to environmental conditions on the sea floor, improving image contrast and clarity is essential for underwater navigation and obstacle avoidance. Particularly in turbid, low-visibility waters, we require robust computer vision techniques and algorithms. Over the past decade, various models for underwater image enrichment have been proposed to address quality and visibility issues under dynamic and natural lighting conditions. This research article aims to evaluate various image improvement methods and propose a robust model that improves image quality, addresses turbidity, and enhances color, ultimately improving obstacle avoidance in autonomous systems. The proposed model demonstrates high accuracy compared to traditional models. The result analysis indicates the proposed model produces images with greatly improved visibility and exceptional color accuracy. Furthermore, research can unlock new possibilities for underwater exploration, monitoring, and intervention by advancing the state-of-the-art models in this domain.

Research on efficient matching method of coal gangue recognition image and sorting image

When the coal gangue sorting robot sorts coal gangue, the position of the target coal gangue will change due to belt slippage, deviation, and speed fluctuations of the belt conveyor. This will cause the robotic to fail in grasping or miss grasping. We have developed a solution to this problem: the IMSSP-Net two-stage network gangue image fast matching method. This method will reacquire the target gangue position information and improve the robot’s grasping precision and efficiency. In the first stage, we use SuperPoint to guarantee the scene adaptability and credibility of feature point extraction. We have enhanced Superpoint’s ability to detect feature points further by using the improved Multi-scale Retinex with Color Restoration enhancement algorithm. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. We eliminated erroneous feature matching point pairs and improved the accuracy of image matching by adopting the PROSAC algorithm. We conducted image matching comparison experiments under different object distances, scales, rotation angles, and complex conditions. The experimental platform adopts the double-manipulator truss-type coal gangue sorting robot independently developed by the team. The matching precision, recall, and matching time of the method are 98.2%, 98.3%, and 84.6ms, respectively. The method can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

Portable alkaloid discrimination via nanozyme-mediated colorimetric paper-based sensor array integrated with smartphone detection.

Apaper-based colorimetric sensor array mediated by a novel nanozyme (CuCo2O4) was developed using a screen-printing technology. The aim was to facilitate the identification of different kinds of alkaloids. Typically, three chromogenic substrates (3,3',5,5'-tetramethylbenzidine, 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid), and o-phenylenediamine) were selected as sensing elements, which can be catalyzed by a CuCo2O4 nanozyme with peroxidase-like activity to yield corresponding oxidized products, thereby inducing color changes. Owing to the varying inhibitory ability of different alkaloids on acetylcholinesterase (AChE), a decrease in choline (Ch) concentration occurs and subsequently results in the restoration of color within the units of sensor array. Color data can be transformed into hue information with a smartphone. The above color variations generated a unique "fingerprint" pattern on five alkaloids (berberine, palmatine, jatrorrhizine, eserine, and harmane), which can be successfully discriminated through linear discriminant analysis in the range0.2 to 20µM. Furthermore, the sensor arrays allowed successful discrimination of the above five alkaloids in Chinese herbal medicine samples and recognition of 22 blind samples. This work presents a novel nanozyme-based paper sensor array, which is a user-friendly and reliable platform for probing different alkaloids. In addition, the developed sensing strategy enables the identification of AChE-related diseases, positively contributing to the screening available of AD-associated drugs.

Denoising sphere‐valued data by relaxed total variation regularization

AbstractCircle‐ and sphere‐valued data play a significant role in inverse problems like magnetic resonance phase imaging and radar interferometry, in the analysis of directional information, and in color restoration tasks. In this paper, we aim to restore ‐sphere‐valued signals exploiting the classical anisotropic total variation on the surrounding ‐dimensional Euclidean space. For this, we propose a novel variational formulation, whose data fidelity is based on inner products instead of the usually employed squared norms. Convexifying the resulting non‐convex problem and using ADMM, we derive an efficient and fast numerical denoiser. In the special case of binary (0‐sphere‐valued) signals, the relaxation is provable tight, that is, the relaxed solution can be used to construct a solution of the original non‐convex problem. Moreover, the tightness can be numerically observed for barcode and QR code denoising as well as in higher dimensional experiments like the color restoration using hue and chromaticity and the recovery of SO(3)‐valued signals.

Color restoration of mural images based on a reversible neural network: leveraging reversible residual networks for structure and texture preservation

AbstractThe Mogao Grottoes in Dunhuang, a treasure of China's and the world's cultural heritage, contains rich historical and cultural deposits and has left precious relics of the history of human art. Over centuries, the Mogao Caves have been affected by natural and human factors, resulting in irreversible fading and discoloration of many murals. In recent years, deep learning technology has shown great potential in the field of virtual mural color restoration. Therefore, this paper proposes a mural image color restoration method based on a reversible neural network. The method first employs an automatic reference selection module based on structural and texture similarity to choose suitable reference mural images for the faded murals. Then, it utilizes a reversible residual network to extract deep features of the mural images without information loss. Next, a channel refinement module is used to eliminate redundant information in the network channels. Finally, an unbiased color transfer module restores the color of the faded mural images. Compared to other image color restoration methods, the proposed method achieves superior color restoration effects while effectively preserving the original structure and texture details of the mural images. Compared to baseline methods, the Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Perception-based Image Quality Evaluator (PIQE) values are improved by 7.97%, 3.46%, and 13.98%, respectively. The color restoration of the Dunhuang Mural holds significant historical, artistic, cultural, and economic values, and plays a positive role in the preservation and inheritance of Chinese culture, as well as in the promotion of cultural exchange and mutual understanding.

Adaptive superpixel segmentation and pigment identification of colored relics based on visible spectral images

This work explores the extraction of the spatial distribution and chemical composition information of pigments in colored relics through visible spectral images. An adaptive superpixel segmentation method is proposed to extract the spatial distribution information of pigments. Quadtree decomposition is applied to generate nonuniform initial seed points based on the image homogeneity. These seed points are used as the initial cluster centers in an extended simple linear iterative clustering (SLIC) algorithm for visible spectral images to create superpixels of varying sizes that reflect the homogeneity. Each superpixel is subsequently treated as an individual area in the colored relics, and a pigment identification method based on the visible spectral reflectance is proposed to identify the pigments in these areas. A standard reference database is constructed using samples that simulate the painting process of ancient wall paintings in the Mogao Grottoes. Geometric features, which are characterized by the linear combination of the normalized visible spectral reflectance, its slope and its curvature, are designed to represent the chemical composition of pigments. The geometric features of the superpixels are compared with those of the pigments in the database using the Euclidean distance to determine the pigments in each area of the colored relics. This work is expected to provide scientific guidance for pigment selection in the color restoration of colored relics.

Application of End-to-End Perception Framework Based on Boosted DETR in UAV Inspection of Overhead Transmission Lines

As crucial predecessor tasks for fault detection and transmission line inspection, insulators, anti-vibration hammers, and arc sag detection are critical jobs. Due to the complexity of the high-voltage transmission line environment and other factors, target detection work on transmission lines remains challenging. A method for high-voltage transmission line inspection based on DETR (TLI-DETR) is proposed to detect insulators, anti-vibration hammers, and arc sag. This model achieves a better balance in terms of speed and accuracy than previous methods. Due to environmental interference such as mountainous forests, rivers, and lakes, this paper uses the Improved Multi-Scale Retinex with Color Restoration (IMSRCR) algorithm to make edge extraction more robust with less noise interference. Based on the TLI-DETR’s feature extraction network, we introduce the edge and semantic information by Momentum Comparison (MoCo) to boost the model’s feature extraction ability for small targets. The different shooting angles and distances of drones result in the target images taking up small proportions and impeding each other. Consequently, the statistical profiling of the area and aspect ratio of transmission line targets captured by UAV generate target query vectors with prior information to enable the model to adapt to the detection needs of transmission line targets more accurately and effectively improve the detection accuracy of small targets. The experimental results show that this method has excellent performance in high-voltage transmission line detection, achieving up to 91.65% accuracy and a 55FPS detection speed, which provides a technical basis for the online detection of transmission line targets.

Rectangling and enhancing underwater stitched image via content-aware warping and perception balancing

Single underwater images often face limitations in field-of-view and visual perception due to scattering and absorption. Numerous image stitching techniques have attempted to provide a wider viewing range, but the resulting stitched images may exhibit unsightly irregular boundaries. Unlike natural landscapes, the absence of reliable high-fidelity references in water complicates the replicability of these deep learning-based methods, leading to unpredictable distortions in cross-domain applications. To address these challenges, we propose an Underwater Wide-field Image Rectangling and Enhancement (UWIRE) framework that incorporates two procedures, i.e., the R-procedure and E-procedure, both of which employ self-coordinated modes, requiring only a single underwater stitched image as input. The R-procedure rectangles the irregular boundaries in stitched images by employing the initial shape resizing and mesh-based image preservation warping. Instead of local linear constraints, we use complementary optimization of boundary-structure-content to ensure a natural appearance with minimal distortion. The E-procedure enhances the rectangled image by employing parameter-adaptive correction to balance information distribution across channels. We further propose an attentive weight-guided fusion method to balance the perception of color restoration, contrast enhancement, and texture sharpening in a complementary manner. Comprehensive experiments demonstrate the superior performance of our UWIRE framework over state-of-the-art image rectangling and enhancement methods, both in quantitative and qualitative evaluation.

Nearly complete hair re-pigmentation in an older patient treated with hydroxyurea for essential thrombocytosis

Introduction Employed in the treatment of malignancies and non-neoplastic conditions, hydroxyurea is associated with integumentary adverse effects, including skin discoloration, xerosis, pruritus, cutaneous atrophy, chronic leg ulcers, oral ulcerations, alopecia, and some nail abnormalities. Case Report A 77-year-old woman was diagnosed with essential thrombocytosis and started on low dose hydroxyurea. After 20 weeks of treatment, she experienced an unexpected change in hair color from gray to dark brown, without using hair dye or supplements. She later developed bilateral dorsal hand melanoderma, melanonychia, and onychodystrophy. Management and outcome It was decided to monitor the patient with no action taken as she was happy with this side effect of hydroxyurea. The platelet count has remained in excellent control. The dark brown hair color persisted over time. Discussion/Conclusion Hair hyperpigmentation likely occurred through melanocyte activation via hydroxyurea. Severe side effects may require dosage adjustments, while milder effects can be monitored closely. The newly observed hair color restoration in this case highlights potential dual (therapeutic and aesthetic) applications of this class of agents.

A Distorted Light Field Image Correction Method Based on Improved Hough Transform

Introduction: In using a camera to take photos, due to environmental limitations, uneven lighting can cause uneven distribution of the image light field, resulting in distortion of the image background and target, blurring of details, and distorted light field images. Method: In view of this, research is conducted on the correction of distorted light field images based on the Hough transform. First, the improved Hough transform is utilized to locate the four coordinates, the matrix information of the normal image is applied to calculate the corresponding parameter amount, and then the low-frequency part of the image spectrum is removed. Finally, it uses the Gaussian function for difference, inputs the original data, and gets the correction result of the distorted light field image. Result: The research results indicate that in the practical application of the distorted light field image correction algorithm based on the Hough transform, the improved Hough transform algorithm is superior to the traditional one. Conclusion: In comparative experiments, the research algorithm outperforms the other three algorithms, with an average color restoration of 93.76% and an average signal-to-noise ratio of 54.22dB. The superiority of the research algorithm has been verified, indicating that the research method can perfectly correct distorted light field images and achieve good correction results.

Color-restoring and energy-saving imaging monitoring for intelligent offshore engineering

Images play a crucial role in artificial intelligence (AI) for offshore engineering, which provide a large amount of data for machine intelligence to ensure the safe operation and maintenance of offshore structures. However, due to the attenuation of sunlight in water or monitoring at nighttime, underwater imaging monitoring systems need a light source, which consumes a lot of energy and impacts the load capacity of an offshore monitoring system. In addition, due to the nonlinearity of attenuation at different wavelengths, underwater images generally suffer from color deviation, tending to be blue and green, which decreases the accuracy of machine learning and intelligent recognition in unmanned offshore engineering. In order to reduce energy consumption and enhance color restoration, we proposed and designed a three-detector underwater imaging system (TD&TP-UIS) based on a prism, in which three detectors were used to enhance the utilization efficiency of light, and a gated band coating was designed on the prism to enhance the color reproduction. A series of experiments were carried out in water tanks, pools, Qiandao Lake, and other places to verify the advantage of color restoration and energy saving using the TD&TP-UIS.

Natural low-illumination image enhancement based on dual-channel prior information

This paper proposes an adaptive image enhancement method that aims to effectively restore the brightness, detail, and natural color of various low-illumination images. To be specific, the method first constructs the initial dual-channel illumination map of the image. Next, the optimal illumination correction coefficient is calculated by the prior information entropy of the initial illumination map, which helps to correct potentially erroneous illumination estimates. To restore the illumination, gamma correction is used with the optimal illumination correction coefficient. Finally, an improved perfect reflection constraint model is used to restore the color of the image. Both visual analysis and quantitative comparison with state-of-the-art methods demonstrate the effectiveness of the method in terms of brightness adjustment, detail recovery, and color restoration.

A Lightweight Neural Network for the Real-Time Dehazing of Tidal Flat UAV Images Using a Contrastive Learning Strategy

In the maritime environment, particularly within tidal flats, the frequent occurrence of sea fog significantly impairs the quality of images captured by unmanned aerial vehicles (UAVs). This degradation manifests as a loss of detail, diminished contrast, and altered color profiles, which directly impact the accuracy and effectiveness of the monitoring data and result in delays in the execution and response speed of monitoring tasks. Traditional physics-based dehazing algorithms have limitations in terms of detail recovery and color restoration, while neural network algorithms are limited in their real-time application on devices with constrained resources due to their model size. To address the above challenges, in the following study, an advanced dehazing algorithm specifically designed for images captured by UAVs over tidal flats is introduced. The algorithm integrates dense convolutional blocks to enhance feature propagation while significantly reducing the number of network parameters, thereby improving the timeliness of the dehazing process. Additionally, an attention mechanism is introduced to assign variable weights to individual channels and pixels, enhancing the network’s ability to perform detail processing. Furthermore, inspired by contrastive learning, the algorithm employs a hybrid loss function that combines mean squared error loss with contrastive regularization. This function plays a crucial role in enhancing the contrast and color saturation of the dehazed images. Our experimental results indicate that, compared to existing methods, the proposed algorithm has a model parameter size of only 0.005 M and a latency of 0.523 ms. When applied to the real tidal flat image dataset, the algorithm achieved a peak signal-to-noise ratio (PSNR) improvement of 2.75 and a mean squared error (MSE) reduction of 9.72. During qualitative analysis, the algorithm generated high-quality dehazing results, characterized by a natural enhancement in color saturation and contrast. These findings confirm that the algorithm performs exceptionally well in real-time fog removal from UAV-captured tidal flat images, enabling the effective and timely monitoring of these environments.

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.

Mathematical processing of RGB data in microfluidic paper-based analytical devices

Microfluidic paper-based analytical devices often are combined with scanners as detectors. In this work, different scanning options offered by scanners: resolution, scanning mode, exposure to radiation, colour restoration, and saving format were tested. Moreover, different attempts to mathematical data treatment based on intensities of three channels—Red, Green and Blue, were studied. All measurements presented in this article were conducted for a model dye—bromothymol blue and a model analyte—zinc(II) ion (complexed with xylenol orange in a paper matrix). The article summarizes the scanning options and possibilities of mathematical calculations. Nevertheless, it is suggested that the best option is to use the prior prepared calculation file to paste obtained intensities and compare all presented in this article (and the most frequently used) equations to process intensities and decide which one should be used in the particular analysis.

A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet

To enhance the accuracy of lightweight CNN classification models in analyzing fish feeding behavior, this paper addresses the image quality issues caused by external environmental factors and lighting conditions, such as low contrast and uneven illumination, by proposing a Multi-step Image Pre-enhancement Strategy (MIPS). This strategy includes three critical steps: initially, images undergo a preliminary processing using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, effectively reducing the impact of water surface reflections and enhancing the visual effect of the images; secondly, the Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc) technique is applied to further improve image contrast, especially in areas of low contrast, by adjusting the local contrast levels to enhance the clarity of the image details; finally, Unsharp Masking (UM) technology is employed to sharpen the images, emphasizing their edges to increase the clarity of the image details, thereby significantly improving the overall image quality. Experimental results on a custom dataset have confirmed that this pre-enhancement strategy significantly boosts the accuracy of various CNN-based classification models, particularly for lightweight CNN models, and drastically reduces the time required for model training compared to the use of advanced ResNet models. This research provides an effective technical route for improving the accuracy and efficiency of an image-based analysis of fish feeding behavior in complex environments.

‘Hair Color Repigmentation in the Recipient Area‘: A Newly Recognized Phenomenon after Hair Transplantation

Background: Hair color repigmentation in the recipient area is a newly detected phenomena after hair transplantation. It’s detected especially in patients with gray or white hairs in the donor area, in which transferring these follicles to the recipient area stimulate them to regain their original blackish color, this may occur due to various stimulants that release during wound healing of slits after hair transplantation that trigger melanin production within the melanocyte inside the hair roots and promote hair repigmentation in the recipient area. Aim: The aim of this study was to detect the frequency of hair color repigmentation phenomena in the recipient area after hair transplantation. Patients and Methods: In this retrospective observational study, we detect thirty-four patients that develop hair color restoration phenomena in the recipient area after undergoing hair transplantation throughout a 1-year follow-up period. 1438 patients that undergo hair transplant between August 2021 and May 2023 were enrolled in this study among them 34 patients with white or gray hair in the donor area were vulnerable to hair color restoration phenomena, each patient was assessed both objectively and subjectively to evaluate the frequency of occurrence of this phenomenon and the percentage of hair follicles restore their original blackish color that transplanted to the recipient area. Results: Thirty-four patients with white or gray hair in the donor area were enrolled in this study that completed 1-year follow-up period. The evaluation score includes (percentage of patients show hair color restoration in the recipient area after hair transplantation, percentage of hair follicles show change in their original color, degree of color restoration among transplanted follicles in the recipient area) and patient’s satisfaction score showed greater positive results in which the majority of the patients with white or gray hair in the donor area restore their original blackish color of their hair after transplanted to the recipient area. Conclusion: Hair color repigmentation in the recipient area is a newly detected phenomena after hair transplantation surgery. Most patients with white or gray hair in the donor area show regain their original blackish color of their hair follicles in the recipient area after hair transplantation. We think that detecting this phenomenon will give us insight about how we can restore the original color of our white or gray hair by understanding the mechanism of how the melanocytes within the hair root regain their ability of melanin production after hair transplantation.

Digital reconstruction of partially lost altarpieces. The case of the Rosary's altarpiece of Sant Pere Màrtir de Manresa

An important challenge of Digital Cultural Heritage is to contribute to the recovery of artworks with their original shape and appearance. Many altarpieces, which are very relevant Christian art elements, have been damaged and/or partly or fully lost. Therefore, the only way to recover them is to carry out their digital reconstruction. Although the procedure that we present here is valid for any altarpiece with similar characteristics, and even for other akin elements, our test bench is the altarpieces that were damaged or destroyed or that disappeared during the Spanish Civil War (1936–1939) in Catalonia, where most suffered these effects. The first step of our work has been the classification of these artworks into different categories on the basis of their degree of destruction and of the available visual information related to each one. This article proposes, for the first time to our knowledge, a workflow for the virtual reconstruction, through photogrammetry, digital modeling, and digital color restoration, of whole altarpieces partially preserved with very little visual information. Our case study is the Rosary’s altarpiece of Sant Pere Màrtir de Manresa church. Currently, this altarpiece is partially preserved in fragments in the Museu Comarcal de Manresa (Spain). But it cannot be reassembled physically owing to the lack of space (actually the church does not exist anymore) and the cost of such an operation. Thus, there is no other solution than the digital one to contemplate and study the altarpiece as a whole. The reconstruction that we provide allows art historians and the general public to virtually see the altarpiece complete and assembled as it was until 1936. The results obtained also allow us to see in detail the reliefs and ornaments of the altarpiece with their digitally restored color.

Low lightness image enhancement based on entropy fidelity and color restoration using YIQ color space

Low lightness image enhancement based on entropy fidelity and color restoration using YIQ color space

Multibranch Wavelet-Based Network for Image Demoiréing.

Moiré patterns caused by aliasing between the camera's sensor and the monitor can severely degrade image quality. Image demoiréing is a multi-task image restoration method that includes texture and color restoration. This paper proposes a new multibranch wavelet-based image demoiréing network (MBWDN) for moiré pattern removal. Moiré images are separated into sub-band images using wavelet decomposition, and demoiréing can be achieved using the different learning strategies of two networks: moiré removal network (MRN) and detail-enhanced moiré removal network (DMRN). MRN removes moiré patterns from low-frequency images while preserving the structure of smooth areas. DMRN simultaneously removes high-frequency moiré patterns and enhances fine details in images. Wavelet decomposition is used to replace traditional upsampling, and max pooling effectively increases the receptive field of the network without losing the spatial information. Through decomposing the moiré image into different levels using wavelet transform, the feature learning results of each branch can be fully preserved and fed into the next branch; therefore, possible distortions in the recovered image are avoided. Thanks to the separation of high- and low-frequency images during feature training, the proposed two networks achieve impressive moiré removal effects. Based on extensive experiments conducted using public datasets, the proposed method shows good demoiréing validity both quantitatively and qualitatively when compared with the state-of-the-art approaches.