Color Restoration Research Articles

Enhancement of Very Low Light Images Using the YIQ Space Based on the CLAHE and Sigmoid Mapping with High Colour Restoration

Enhancement of Very Low Light Images Using the YIQ Space Based on the CLAHE and Sigmoid Mapping with High Colour Restoration

Quantitative analysis methods for evaluating colour on architectural heritage: survey of colour restoration and perception of the Wen Yuan Ge in the Forbidden City

Three methods can be used to evaluate the colour used on architectural heritage: manual evaluation, instrumental measurement, and process restoration. Optical instruments that can obtain relatively accurate chromaticity data are ideal. As architectural heritage sites age, their colour fluctuates. Researchers must screen colour data and analyse the features based on experience with colour perception. This study combines quantitative colour analysis and a scaled perception survey to analyse the Wen Yuan Ge in the Forbidden City and provide a new reference for researching and designing colour in architectural heritage.

CAT: A lightweight Color-aware Transformer for sandstorm image enhancement

Sandstorm images are characterized by color casts and reduced contrast due to the presence of suspended sand particles, which significantly impacts the performance of high-level computer vision tasks. Recently, numerous deep learning-based methods have been proposed for sandstorm image enhancement. However, most of them are either ineffective or have excessive parameters. In this paper, we introduce a lightweight Color-aware Transformer (CAT) for sandstorm image enhancement. Specifically, we propose the Color Restoration Module (CRM), which integrates Channel Self-attention Transformer (CST) and Vision Transformer (ViT) to effectively correct color distortion by utilizing global channel information. Additionally, the Feature Refinement Module (FRM), composed of cascaded attention blocks, is designed to dynamically capture the most valuable information to refine the features. By leveraging the aforementioned modules, our network is capable of processing an image with 256 × 256 resolution in just 0.06 s, while maintaining a compact architecture of only 2.1M parameters. Through extensive experiments on both synthetic and real-world sandstorm datasets, we demonstrate that our CAT outperforms several state-of-the-art methods in terms of image quality and computational efficiency.

AoSRNet: All-in-One Scene Recovery Networks via multi-knowledge integration

Scattering and attenuation of light in no-homogeneous imaging media or inconsistent light intensity will cause insufficient contrast and color distortion in the collected images, which limits the developments such as vision-driven smart urban, autonomous vehicles, and intelligent robots. In this paper, we propose an all-in-one scene recovery network via multi-knowledge integration (termed AoSRNet) to improve the visibility of imaging devices in typical low-visibility imaging scenes (e.g., haze, sand dust, underwater, and low light). It combines gamma correction (GC) and optimized linear stretching (OLS) to create the detail enhancement module (DEM) and color restoration module (CRM). Additionally, we suggest a multi-receptive field extraction module (MEM) to attenuate the loss of image texture details caused by GC nonlinear and OLS linear transformations. Finally, we refine the coarse features generated by DEM, CRM, and MEM through Encoder-Decoder to generate the final restored image. Comprehensive experimental results demonstrate the effectiveness and stability of AoSRNet compared to other state-of-the-art methods. The source code is available at https://github.com/LouisYuxuLu/AoSRNet.

Magnetic Flux Leakage Defect Identification Method for Small-Diameter Pipeline Elbow Based on the Improved YOLOv5

Abstract The elbow is an important constituent of oil and gas pipeline systems and plays a key role in changing the direction of pipelines. Corrosion defects pose a significant risk to the safe operation of elbows. Magnetic flux leakage (MFL) detection has been developed as a suitable technique for identifying defects in pipelines. To address the distortion of elbow defect signals in the images arising from variations in the liftoff value of the leakage detector, this paper proposed an image identification method based on an improved YOLOv5 network. The differences in defect images are simulated by analyzing the liftoff value of the magnetization unit. A defect image enhancement method of multiscale retinex with color restoration fusion homomorphic filtering (MSRCR-HF) is employed to enhance the features of defective MFL signal images. To further improve the accuracy of the model, the YOLOv5 network is optimized by integrating the convolutional block attention module (CBAM) and the space-to-depth-nonstrided convolution (SPD-Conv) module. The results show that the proposed image enhancement method effectively accentuates the features of defect images. Moreover, the suggested image identification method exhibits superior accuracy in identification. The mean average precision (mAP) values for the original image set and the enhanced image set are 85.0% and 91.4%, respectively. Consequently, the proposed method is shown to be highly viable for the automatic identification of MFL defects in small-diameter pipe elbows.

YOLO-BLBE: A Novel Model for Identifying Blueberry Fruits with Different Maturities Using the I-MSRCR Method

Blueberry is among the fruits with high economic gains for orchard farmers. Identification of blueberry fruits with different maturities has economic significance to help orchard farmers plan pesticide application, estimate yield, and conduct harvest operations efficiently. Vision systems for automated orchard yield estimation have received growing attention toward fruit identification with different maturity stages. However, due to interfering factors such as varying outdoor illuminations, similar colors with the surrounding canopy, imaging distance, and occlusion in natural environments, it remains a serious challenge to develop reliable visual methods for identifying blueberry fruits with different maturities. This study constructed a YOLO-BLBE (Blueberry) model combined with an innovative I-MSRCR (Improved MSRCR (Multi-Scale Retinex with Color Restoration)) method to accurately identify blueberry fruits with different maturities. The color feature of blueberry fruit in the original image was enhanced by the I-MSRCR algorithm, which was improved based on the traditional MSRCR algorithm by adjusting the proportion of color restoration factors. The GhostNet model embedded by the CA (coordinate attention) mechanism module replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-BLBE model. The BIFPN (Bidirectional Feature Pyramid Network) structure was applied in the neck network of the YOLO-BLBE model, and Alpha-EIOU was used as the loss function of the model to determine and filter candidate boxes. The main contributions of this study are as follows: (1) The I-MSRCR algorithm proposed in this paper can effectively amplify the color differences between blueberry fruits of different maturities. (2) Adding the synthesized blueberry images processed by the I-MSRCR algorithm to the training set for training can improve the model’s recognition accuracy for blueberries of different maturity levels. (3) The YOLO-BLBE model achieved an average identification accuracy of 99.58% for mature blueberry fruits, 96.77% for semi-mature blueberry fruits, and 98.07% for immature blueberry fruits. (4) The YOLO-BLBE model had a size of 12.75 MB and an average detection speed of 0.009 s.

Image Color Balance with Laplacian and Gaussian Pyramid (CBLGP) Algorithm

In recent times, technology has rapidly and significantly evolved across all sectors. Digital image processing stands out as a modern technology aimed at achieving clear images. However, digitized images often encounter issues of low quality, such as unclear or underwater images that require enhancement for better visibility. These problems stem from factors like deficient focusing, lighting, and various constraints leading to low contrast, shading, and artifacts. Underwater and satellite images consistently face less-than-ideal conditions due to environmental factors like light refraction in water, particle scattering, and dust in aquatic environments. Similarly, challenges in space, such as poor illumination and lack of contrast, further complicate image analysis. Overcoming these obstacles is crucial for extracting valuable information, necessitating advanced processing techniques. This paper introduces an enhanced Gaussian/Laplace color balance-fusion algorithm designed to improve image visibility. Modifications to certain equations result in sharper and clearer images. The algorithm begins by determining the white balance of the input RGB color image and subsequently enhances its intensity. Edge improvement is carried out separately using a depth filter. The weights for each image are then determined and combined to form a Laplace Pyramid. A color restoration technique is applied to process the resulting image, producing the final enhanced image. While existing methods for image contrast enhancement typically focus on image features, they often neglect user characteristics. This paper explores the application of image sharpening, a prominent image enhancement technique, in clearing underwater or low-quality images using the proposed algorithm. Keywords: EDSHE, High pass filter, White patch ratix, Laplacian pyramid, Color restoration.

Experimental and Theoretical Study of Madder-Associated Dyestuffs on Silk: Adsorption of Kinetics, Thermodynamics and Molecular Docking

ABSTRACT In this work, dyed samples were self-prepared from two methods including madder dyeing and mordant dyeing by madder combined with Al3+ at different temperatures, and their kinetics and isothermal adsorption were both discussed. Weber–Morris diffusion model and Freundlich model were found to be more suitable in dyeing for kinetics and thermodynamics. Through SEM, FTIR and fastness tests, the dyeing performances were displayed and the possible structure between madder dyes and the complex with Al3+ were illustrated. By molecular docking, madder dyes showed combination with glutamic acid predominantly. After introducing metal ion, the shortage of hydrogen bonds length was observed. It was initially employed to elucidate the potential binding modes between dyes and proteinaceous fibers. Overall, through experimental dyeing and curve fitting, this study provided insights into the dyeing mechanism of madder-associated dyestuffs on silk. Moreover, molecular docking elucidated the dyeing mechanism further, highlighting that the presence of metal ion enhances the firmness of the staining. The elucidation of dyeing mechanism not only provided valuable theoretical support for color restoration but also shed light on the practically modify of methods in obtaining ideal color. These results could serve as inspiration for conservators to maintain the vibrancy and longevity of dyed silks.

Generation of PAS-stained images of glomerular tissue units using a generative adversarial network with spectral normalization colorization method

In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.

Coordinate-Aware Mask R-CNN with Group Normalization: A underwater marine animal instance segmentation framework

Unsustainable fishing, driven by bycatch and discards, harms marine ecosystems. Addressing this, we propose a Coordinate-Aware Mask R-CNN (CAM-RCNN) method to enhance fish detection in commercial trawls. Leveraging CoordConv and Group Normalization, our approach improves generalisation and stability. To tackle class imbalance, a compound Dice and cross-entropy loss is employed, and image data are enhanced through multi-scale retinex and colour restoration. Evaluating on two fishing datasets, CAM-RCNN excels in accuracy and generalisation, achieving the best Average Precision (AP) for instance mask and BBOX prediction in both source (39.7%, 40.2%) and target domains (24.4%, 24.2%). This method promotes sustainable fishing by selectively capturing desired fish, reducing harm to non-target species.

Enhancement of Lightness Image Based on DCP with Color Restoration and Lightness Mapping

Enhancement of Lightness Image Based on DCP with Color Restoration and Lightness Mapping

Generation of PAS-Stained images of glomerular tissue units using a generative adversarial network with spectral normalization colorization method

In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.

Multi-scale fusion for image enhancement in shield tunneling: a combined MSRCR and CLAHE approach

Aiming at addressing the complex lighting conditions in shield construction environments and enhancing the contrast of low-light images while preserving image details, this paper presents an image enhancement technique based on multi-scale fusion. The proposed method first duplicates the input image into two copies. Subsequently, the first copy undergoes color and contrast adjustment via the adaptive weighted Retinex algorithm for color restoration (A-MSRCR) algorithm. Meanwhile, the second copy undergoes contrast enhancement and detail preservation by combining Gamma correction and Contrast Limited Adaptive Histogram Equalization (CLAHE). Finally, a normalized weight map is introduced to facilitate multi-scale fusion of the two enhanced images, ensuring an overall enhancement in both contrast and detail. The experimental results show that the proposed method provides better visual quality and objective data than the compared methods. Moreover, it offers valuable support for subsequent recognition and localization tasks.

Image dehazing combining polarization properties and deep learning.

In order to solve the problems of color shift and incomplete dehazing after image dehazing, this paper proposes an improved image self-supervised learning dehazing algorithm that combines polarization characteristics and deep learning. First, based on the YOLY network framework, a multiscale module and an attention mechanism module are introduced into the transmission feature estimation network. This enables the extraction of feature information at different scales and allocation of weights, and effectively improves the accuracy of transmission map estimation. Second, a brightness consistency loss based on the YCbCr color space and a color consistency loss are proposed to constrain the brightness and color consistency of the dehazing results, resolving the problems of darkened brightness and color shifts in dehazed images. Finally, the network is trained to dehaze polarized images based on the atmospheric scattering model and loss function constraints. Experiments are conducted on synthetic and real-world data, and comparisons are made with six contrasting dehazing algorithms. The results demonstrate that, compared to the contrastive dehazing algorithms, the proposed algorithm achieves PSNR and SSIM values of 23.92 and 0.94, respectively, on synthetic image samples. For real-world image samples, color restoration is more authentic, contrast is higher, and detailed information is richer. Both subjective and objective evaluations show significant improvements. This validates the effectiveness and superiority of the proposed dehazing algorithm.

Power equipment image enhancement processing based on YOLO-v8 target detection model under MSRCR algorithm

Abstract With the rapid development of the power industry, higher requirements have been put forward for real-time monitoring and fault identification of power equipment. However, images of power equipment in actual scenes are often affected by problems such as uneven illumination and color distortion, leading to a decrease in the performance of the target detection model. Hence, this paper suggests merging the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm with the YOLO-v8 target detection model to enhance the visual quality of power equipment images and boost the accuracy and efficiency of target detection. Initially, the MSRCR algorithm enhances image brightness, contrast, and color restoration and preserves edge and detail features. Subsequently, the paper explores the architecture of YOLO-v8, incorporating the SE (Squeeze-and-Excitation) attention mechanism. This mechanism dynamically adjusts channel weights to optimize feature processing in input data. The final experimental results show that using the MSRCR algorithm to enhance the data and combining it with the SE attention mechanism have improved by about 3.2% compared to the original YOLO-v8 model. In comparative experiments with other algorithms, the method proposed in this article achieved an accuracy of 94.3% and a recall rate of 92.6%, which are both higher than other models. By enhancing power equipment images with the MSRCR algorithm, the YOLO-v8 model has significantly improved both target detection accuracy and recall rate. In summary, the MSRCR power equipment image enhancement processing method proposed in this article based on the YOLO-v8 target detection model can effectively improve the visual quality of power equipment images and improve the accuracy and efficiency of target detection.

Texture-aware and color-consistent learning for underwater image enhancement

Texture and color are pivotal factors for evaluating the quality of underwater image enhancement. However, current methods for enhancing underwater images still exhibit deficiencies in the restoration of texture and color. Diverging from previous approaches, we conduct heuristic modeling specifically targeting color and texture, culminating in the proposal of a texture-aware and color-consistent network (TACC-Net). Specifically, it first obtains preliminary enhanced images of more global features to facilitate feature decoupling. Then, we designed a color feature decoupling (CFD) sub-module to decouple the preliminary enhanced images into color-consistent color histograms to generate textured gray images. Finally, we designed a color space adjustment embedding (CSAE) sub-module, which is used to evaluate the similarity between features when embedding color information, and adjust the embedding of color information on textures. The experimental results indicate that our method enhances images and exhibits strong competitiveness in improving visual quality.

Color restoration of images through high order zeroing neural networks

One of the fundamental tasks in pattern recognition is color image restoration. Every color image has three channels in the RGB color space, in contrast to grayscale images. The restoration of color images is typically far more challenging than that of grayscale images because of the internal relationships among the three channels. Given that the color image restoration can be represented as a dynamic problem with quaternion matrices, a new high order zeroing neural network (HZNN) model is developed to tackle this issue. Specifically, the time-varying quaternion matrix linear equations can be solved using the HZNN design, which is a member of the family of zeroing neural network (ZNN) models that correlate to hyperpower iterative techniques. In a realistic color image restoration application, the HZNN design outperforms the ZNN design, although both approaches work amazingly well.

Effect of casein phosphopeptide-amorphous calcium phosphate, Riva Star, and Moringa oleifera-based nano-silver fluoride on caries affected dentin remineralization bonded to composite resin.

This study aims to assess the effects of the most recent remineralizing agents, casein phosphopeptide-amorphous calcium phosphate (CPP-ACP), CO2 laser irradiation + topical fluoride (CO2 laser + TF), and Nanosilver fluoride - M. oleifera (NSF-MOLE), on the shear bond strength (SBS) and bond failure between resin composite and remineralized caries affected dentin (CAD). Fifty human molars with occlusal caries reaching approximately halfway through the dentin were immersed in a 4% thymol solution. The infected dentin was removed using an excavator and the CAD surface was exposed. The sample was allocated into five groups (n=10) based on the remineralizing agent applied. Group 1: no remineralizing agent, group 2: CPP-ACP, group 3: Riva Star, group 4: NSF:MOLE, and group 5: (CO2 laser + TF). The shear bond testing procedure was conducted utilizing a universal testing machine and a stereo-microscope was used to study the failure pattern. The researchers utilized a one-way analysis of variance. The Tukey post hoc test was conducted for multiple comparison tests. Group 4 (NSF-MOLE) (13.77±1.94 MPa) treated testers recognized the highest bond values of tooth color restoration to the CAD surface. Nonetheless, group 1 test samples with no mineralization unveiled the minimum outcome of bond integrity (9.12±1.14 MPa). Intergroup comparison exploration showed that group 2 (CPP-ACP), group 4 (NSF-MOLE) (13.77±1.94 MPa), and group 5 (CO2 laser + TF) established comparable values of SBS. Furthermore, group 3 (Riva Star) displayed better SBS than group 1 but lower than group 2, group 4, and group 5. Remineralization of CAD using modern regimes (CPP-ACP, NSF-MOLE, and CO2 laser + TF has the potential to be used to enhance the bond strength of CAD to composite restoration.

Sea-Pix-GAN: Underwater image enhancement using adversarial neural network

In the last decade, the exploration of deep-sea ecosystems has surged, offering exciting prospects for discovering untapped resources such as medical drugs, food and energy sources, and renewable energy products. Consequently, research in underwater image processing has witnessed substantial growth. However, underwater imaging poses significant challenges, particularly without sophisticated, specialized cameras. Traditional cameras are impacted by absorption and scattering in the aquatic environment, producing hazy images with a blue–green tint. This phenomenon holds implications for marine research and other disciplines that rely on underwater imaging. While hardware advancements have been made over the years, image processing remains a valuable, cost-effective, and practical approach for underwater enhancement. Despite the existence of state-of-the-art techniques for underwater enhancement and restoration, their performance is often inconsistent. While some methods excel in contrast restoration, color restoration remains a pervasive challenge. In this paper, we introduce Sea-Pix-GAN, a Generative Adversarial Network (GAN)-based model that addresses these issues in underwater image enhancement. We redefine the problem as an image-to-image translation task and tailor the objective and loss functions to achieve color, content, and style transfer. The model is trained on a large dataset of underwater scenes, encompassing the diverse color dynamics of underwater subjects. Sea-Pix-GAN demonstrates promising results in restoring color, contrast, texture, and saturation. To validate its effectiveness, we compare the performance of Sea-Pix-GAN quantitatively based on metrics like PSNR, SSIM, and UIQM and qualitatively against several existing techniques.

A virtual restoration network of ancient murals via global–local feature extraction and structural information guidance

Ancient murals are precious cultural heritages. They suffer from various damages due to man-made destruction and long-time exposure to the environment. It is urgent to protect and restore the damaged ancient murals. Virtual restoration of ancient murals aims to fill damaged mural regions by using modern computer techniques. Most existing restoration approaches fail to fill the loss mural regions with rich details and complex structures. In this paper, we propose a virtual restoration network of ancient murals based on global–local feature extraction and structural information guidance (GLSI). The proposed network consists of two major sub-networks: the structural information generator (SIG) and the image content generator (ICG). In the first sub-network, SIG can predict the structural information and the coarse contents for the missing mural regions. In the second sub-network, ICG utilizes the predicted structural information and the coarse contents to generate the refined image contents for the missing mural regions. Moreover, we design an innovative BranchBlock module that can effectively extract and integrate the local and global features. We introduce a Fast Fourier Convolution (FFC) to improve the color restoration for the missing mural regions. We conduct experiments over simulated and real damaged murals. Experimental results show that our proposed method outperforms other three comparative state-of-the-art approaches in terms of structural continuity, color harmony and visual rationality of the restored mural images. In addition, the mural restoration results of our method can achieve comparatively high quantitative evaluation metrics.