Color Distortion Problem Research Articles

Improved transfer learning two-branch convolutional neural network image dehazing

In view of the existing image dehazing algorithms, there are problems of incomplete dehazing and image color distortion. A dehazing network combining Transfer learning sub-net and Residual attention sub-net is proposed. First, the pretrained model of the transfer learning subnet is adopted to enhance the feature attributes of the samples. Second, the struct ure of the dual-branch network is constructed, and the residual attention sub-network is used to assist the transfer learning subnetwork to train the parameters of the network model. Finally, the method of tail ensemble learning is used to fuse the features of the dual network to obtain the model parameters of the dehazed image, so as to complete the image restoration task. The experimental results show that the algorithm proposed in the paper improves the PSNR index by 1.87 dB and 4.22 dB on the RESIDE data set and the O-HAZE data set respectively, and the SSIM index on the O-HAZE data set by 6.7%.

Virtual Restoration of Ancient Mold-Damaged Painting Based on 3D Convolutional Neural Network for Hyperspectral Image

Painted cultural relics hold significant historical value and are crucial in transmitting human culture. However, mold is a common issue for paper or silk-based relics, which not only affects their preservation and longevity but also conceals the texture, patterns, and color information, hindering cultural value and heritage. Currently, the virtual restoration of painting relics primarily involves filling in the RGB based on neighborhood information, which might cause color distortion and other problems. Another approach considers mold as noise and employs maximum noise separation for its removal; however, eliminating the mold components and implementing the inverse transformation often leads to more loss of information. To effectively acquire virtual restoration for mold removal from ancient paintings, the spectral characteristics of mold were analyzed. Based on the spectral features of mold and the cultural relic restoration philosophy of maintaining originality, a 3D CNN artifact restoration network was proposed. This network is capable of learning features in the near-infrared spectrum (NIR) and spatial dimensions to reconstruct the reflectance of visible spectrum, achieving the virtual restoration for mold removal of calligraphic and art relics. Using an ancient painting from the Qing Dynasty as a test subject, the proposed method was compared with the Inpainting, Criminisi, and inverse MNF transformation methods across three regions. Visual analysis, quantitative evaluation (the root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MEA), and a classification application were used to assess the restoration accuracy. The visual results and quantitative analyses demonstrated that the proposed 3D CNN method effectively removes or mitigates mold while restoring the artwork to its authentic color in various backgrounds. Furthermore, the color classification results indicated that the images restored with 3D CNN had the highest classification accuracy, with overall accuracies of 89.51%, 92.24%, and 93.63%, and Kappa coefficients of 0.88, 0.91, and 0.93, respectively. This research provides technological support for the digitalization and restoration of cultural artifacts, thereby contributing to the preservation and transmission of cultural heritage.

Transformer Discharge Carbon-Trace Detection Based on Improved MSRCR Image-Enhancement Algorithm and YOLOv8 Model.

It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot.

An Image Dehazing Algorithm for Underground Coal Mines Based on gUNet.

Aiming at the problems of incomplete dehazing, color distortion, and loss of detail and edge information encountered by existing algorithms when processing images of underground coal mines, an image dehazing algorithm for underground coal mines, named CAB CA DSConv Fusion gUNet (CCDF-gUNet), is proposed. First, Dynamic Snake Convolution (DSConv) is introduced to replace traditional convolutions, enhancing the feature extraction capability. Second, residual attention convolution blocks are constructed to simultaneously focus on both local and global information in images. Additionally, the Coordinate Attention (CA) module is utilized to learn the coordinate information of features so that the model can better capture the key information in images. Furthermore, to simultaneously focus on the detail and structural consistency of images, a fusion loss function is introduced. Finally, based on the test verification of the public dataset Haze-4K, the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are 30.72 dB, 0.976, and 55.04, respectively, and on a self-made underground coal mine dataset, they are 31.18 dB, 0.971, and 49.66, respectively. The experimental results show that the algorithm performs well in dehazing, effectively avoids color distortion, and retains image details and edge information, providing some theoretical references for image processing in coal mine surveillance videos.

A Retinex-based network for image enhancement in low-light environments.

Most of the existing low-light image enhancement methods suffer from the problems of detail loss, color distortion and excessive noise. To address the above-mentioned issues, this paper proposes a neural network-based low-light image enhancement network. The network is divided into three parts: decomposition network, reflection component denoising network, and illumination component enhancement network. In the decomposition network, the input image is decomposed into a reflection image and an illumination image. In the reflection component denoising network, the Unet3+ network improved by fusion CA attention is adopted to denoise the reflection image. In the illumination component enhancement network, the adaptive mapping curve is adopted to enhance the illumination image iteratively. Finally, the processed illumination and reflection images are fused based on Retinex theory to obtain the final enhanced image. The experimental results show that the proposed network achieves excellent visual effects in subjective evaluation. Additionally, it shows a significant improvement in objective evaluation metrics, including PSNR, SSIM, NIQE, and so on, when compared to the results in several public datasets.

An unsupervised low-light image enhancement method for improving V-SLAM localization in uneven low-light construction sites

Construction robots have been increasingly adopted in construction projects to improve productivity, reduce risk, and speed up work cycles. Visual Simultaneous Localization and Mapping (V-SLAM) technology is widely used in construction robots due to its lightweight weight, cost-effectiveness, and provision of semantic information. On real construction sites, lighting conditions are often challenging due to factors such as uneven lighting intensity, inadequate exposure, or robots in backlit positions (e.g. roughcast houses without artificial lighting, underground car parks), which cause images captured under these conditions to exhibit low signal-to-noise ratio, uneven lighting, color distortion, noise, and other problems. These challenges make the extraction and matching of feature points in V-SLAM difficult, resulting in significant positioning errors or the inability to generate positioning trajectories for construction robotics. Although existing methods for enhancing low-light images can certainly improve image brightness, they still cannot effectively address the localization challenges brought about by low-light construction scenes with uneven illumination and noise. To address the interference of uneven and changing lighting conditions on V-SLAM localization in construction sites, this paper proposes the Unsupervised Reflectance Retinex and Noise model (URRN-Net) to enhance low-light construction images. By using a CNN model based on the Retinex theory to decompose the illumination characteristics of images, we achieve effective brightness restoration of uneven low-light images by utilizing unsupervised loss functions. URRE-Net can reduce the root mean square localization error by >65% compared to low-light images in the ORB-SLAM3 method, and the maximum error is reduced by >71% in on-site experiments. The proposed URRE-Net can be integrated with existing V-SLAM algorithms to provide more robust localization services for low-light construction site applications such as building operations (e.g., interior wall spraying robotic) or construction management tasks (e.g., automatic tunnel inspection).

Detection of Illegal Goods using X-ray Image Enhancement Algorithm

Abstract: In order to facilitate the security personnel to identify the items in the X-ray image, it is necessary to enhance the security X-ray image to highlight the color, edge, shape and other details of the different items in the image, so that the security personnel can identify the inspected items more accurately and quickly, to ensure the safe operation of the airport. Aiming at the problem of color distortion in CLAHE enhanced airport security X-ray images, an X-ray image enhancement algorithm combining USM+CLAHE is proposed. In this work, First, we calculate the grayscale images on the R, G, and B channels of the X-ray image and perform Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement respectively, and then merge the enhanced R, G, and B grayscale images. Next, we perform the Unsharp Mask (USM) sharpening operation on the Xray image enhanced by CLAHE, and finally merge the original image and the USM sharpened image according to the weight. The experimental results show that the USM+CLAHE algorithm can effectively enhance the security X-ray image, and at the same time can suppress the color distortion of the enhanced image.

Cycle-Retinex: Unpaired Low-Light Image Enhancement via Retinex-Inline CycleGAN

Low-light image enhancement aims to recover normal-light images from the images captured under dim environments. Most existing methods could just improve the light appearance globally whereas failing to handle other degradation such as dense noise, color offset and extremely low-light. Moreover, unsupervised methods proposed in recent years lack reliable physical model as the basis, thus universality is greatly limited. To address these problems, we propose a novel low-light image enhancement method via Retinex-inline cycle-consistent generative adversarial network named Cycle-Retinex, whose training is totally dependent on unpaired datasets. Specifically, we organically combine Retinex theory with CycleGAN, by which we decouple low-light image enhancement task into two sub-tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> illumination map enhancement and reflectance map restoration. Retinex theory helps CycleGAN simplify low-light image enhancement problem and CycleGAN provides synthetic paired images to guide the training of Retinex decomposition network. We further introduce a self-augmented method to address the color distortion and noise problem, thus making the network learn to enhance low-light images adaptively. Extensive experiments show that the proposed method can achieve promising results. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mummmml/Cycle-Retinex</uri> .

Underwater image recovery based on water type estimation and adaptive color correction

In underwater environments, light propagation is affected by scattering and absorption, leading to color distortion and quality degradation of underwater images. In addition, the presence of a color cast in the image and variations in the attenuation coefficients across various water bodies bring great challenges for underwater image restoration. In this paper, an underwater image restoration method based on water body type estimation and adaptive color correction is proposed. Initially, the underwater images are categorized into color casts and non-color casts according to their hue, and a water body type estimation method based on image color and blurriness is introduced for improving the accuracy of transmission map estimation. Following this, we performed adaptive color correction on the image using a nonlinear transformation, which effectively eliminates color cast. Then the background light position is corrected using the degree of color cast of the image to restore the hue and brightness of the image more naturally. Ultimately, the acquired background light and transmission map are utilized to generate clear underwater images using the image formation model (IFM). Experiments on the widely used UIEB benchmark and SUID datasets show that our method effectively solves the problems of image color distortion and quality degradation, generating satisfactory visual effects.

Multi-Scale Attention Feature Enhancement Network for Single Image Dehazing.

Aiming to solve the problem of color distortion and loss of detail information in most dehazing algorithms, an end-to-end image dehazing network based on multi-scale feature enhancement is proposed. Firstly, the feature extraction enhancement module is used to capture the detailed information of hazy images and expand the receptive field. Secondly, the channel attention mechanism and pixel attention mechanism of the feature fusion enhancement module are used to dynamically adjust the weights of different channels and pixels. Thirdly, the context enhancement module is used to enhance the context semantic information, suppress redundant information, and obtain the haze density image with higher detail. Finally, our method removes haze, preserves image color, and ensures image details. The proposed method achieved a PSNR score of 33.74, SSIM scores of 0.9843 and LPIPS distance of 0.0040 on the SOTS-outdoor dataset. Compared with representative dehazing methods, it demonstrates better dehazing performance and proves the advantages of the proposed method on synthetic hazy images. Combined with dehazing experiments on real hazy images, the results show that our method can effectively improve dehazing performance while preserving more image details and achieving color fidelity.

A lightweight object detection framework for underwater imagery with joint image restoration and color transformation

Traditional underwater detectors struggle to match the demands of visual observation and icon detection because of the underwater optical pictures' color distortion, uneven illumination, and excessive noise levels. In this paper, a lightweight deep-water object detection network is proposed, which consists of three key components, namely image restoration, color transformation, and object detection. First, we propose a lightweight attention module for the problems of color distortion and uneven illumination in underwater images. This module pays more attention to the areas with a greater influence of underwater color when dealing with different types of poor-quality images. Second, to reduce the computational complexity of object detection, we propose a color conversion module based on the laplacian pyramid decomposition. This module incorporates multi-scale features to effectively address the loss of details in the brightest or darkest regions. Finally, we propose a multi-scale dense residual object detection network. The network applies a multi-scale kernel to an improved residual module to realize the extraction and fusion of feature information of multiple receptive fields. Furthermore, to deal with the problem of underwater small-sample object detection, we propose a mutually exclusive loss function. The mutual exclusion loss promotes the update of network parameters so that the classes with different characteristics of samples generated by the network are mutually exclusive. We ran tests on the Pycharm platform to show that the suggested lightweight joint underwater object identification algorithm outperformed the most sophisticated ones. The proposed system was put to the test in an intricate underwater setting, achieving 92.56 percent accuracy and 19.2 frames per second.

Multi‐cues underwater image restoration algorithm combined with light field technology

AbstractUnderwater images suffer from color distortion, low clarity and halos problems due to light absorption, particle scattering and non‐uniform illumination. To address these degradation issues, a multi‐cues underwater image restoration algorithm combined with light field technology is proposed. First, based on Epipolar Plane Image, the light field cue transmittance containing depth information is calculated. Then, according to the turbidity of the underwater image, the light field cue transmittance and the polarization cue transmittance are fused to obtain the multi‐cues transmittance, which can effectively reduce the effect of particle scattering and color bias. Finally, the background light is estimated through the all‐focus operation, which can effectively overcome the distortion of an underwater single image and simultaneously reduce the halo phenomenon. Experimental results show that the method achieves the best results evaluated by UCIQE, UIQM, PSNR, and SSIM, and the restored color under the method is closer to the actual image than other underwater restoration methods.

Image Enhancement Algorithm of Foggy Sky with Sky based on Sky Segmentation

In recent years, image defogging has become a research hotspot in the field of digital image processing. Through defogging enhancement processing, the visual quality of foggy images can be significantly improved, and it is also an important part of subsequent image processing. To overcome the limitations of traditional image dehazing, an image enhancement algorithm for foggy image with sky based on sky segmentation is proposed. Firstly, based on K-means clustering and sky feature analysis, sky region recognition is performed for fog image with sky. Secondly, according to the pixels of sky region, the rough transmittance is corrected, and the dehazing image is obtained by dark channel prior image dehazing based on guided filter. Finally, the dehazing image is equalized by bi-histogram equalization. This algorithm effectively avoids the problem of color distortion and halo caused by the traditional dark channel prior dehazing algorithm in the sky area, and makes the restored foggy image have better global and local contrast.

Area Contrast Distribution Loss for Underwater Image Enhancement

In this paper, we aim to design a lightweight underwater image enhancement algorithm that can effectively solve the problem of color distortion and low contrast in underwater images. Recently, enhancement methods typically optimize a perceptual loss function, using high-level features extracted from pre-trained networks to train a feed-forward network for image enhancement tasks. This loss function measures the perceptual and semantic differences between images, but it is applied globally across the entire image and does not consider semantic information within the image, which limits the effectiveness of the perceptual loss. Therefore, we propose an area contrast distribution loss (ACDL), which trains a flow model to achieve real-time optimization of the difference between output and reference in training. Additionally, we propose a novel lightweight neural network. Because underwater image acquisition is difficult, our experiments have shown that our model training can use only half the amount of data and half the image size compared to Shallow-UWnet. The RepNet network reduces the parameter size by at least 48% compared to previous algorithms, and the inference time is 5 times faster than before. After incorporating ACDL, SSIM increased by 2.70% and PSNR increased by 9.72%.

A spatiotemporal transferable image fusion technique for GeoEye-1 satellite imagery

This study proposed a novel technique to solve the problem of color distortion in the fusion of the GeoEye-1 satellite's panchromatic (PAN) and multispectral (MS) images. This technique suggested reducing the difference in radiometry between the PAN and MS images by using modification coefficients for the MS bands in the definition of the intensity (I) equation, which guarantees using only the overlapped wavelengths with the PAN band. These modification coefficients achieve spatiotemporal transferability for the proposed fusion technique. As the reflectance of vegetation is high in the NIR band and low in the RGB bands, this technique suggested using an additional coefficient for the NIR band in the definition of the I equation, which varies based on the ratio of the agricultural features within the image, to indicate the correct impact of vegetation. This vegetation coefficient provides stability for the proposed fusion technique across all land cover classes. This study used three datasets of GeoEye-1 satellite PAN and MS images in Tanta City, Egypt, with different land cover classes (agricultural, urban, and mixed areas), to evaluate the performance of this technique against five different standard image fusion techniques. In addition, it was validated using six additional datasets from different locations and acquired at different times to test its spatiotemporal transferability. The proposed fusion technique demonstrated spatiotemporal transferability as well as great efficiency in producing fused images of superior spatial and spectral quality for all types of land cover.

Self-Supervised Pre-Training Joint Framework: Assisting Lightweight Detection Network for Underwater Object Detection

In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded in unmanned underwater vehicles. This paper proposes a lightweight underwater object detection network based on the YOLOX model called LUO-YOLOX. A novel weighted ghost-CSPDarknet and simplified PANet were used in LUO-YOLOX to reduce the parameters of the whole model. Moreover, aiming to solve the problems of color distortion and unclear features of targets in underwater images, this paper proposes an efficient self-supervised pre-training joint framework based on underwater auto-encoder transformation (UAET). After the end-to-end pre-training process with the self-supervised pre-training joint framework, the backbone of the object detection network can extract more essential and robust features from degradation images when retrained on underwater datasets. Numerous experiments on the URPC2021 and detecting underwater objects (DUO) datasets verify the performance of our proposed method. Our work can assist unmanned underwater vehicles to perform underwater object detection tasks more accurately.

A hue preservation lossless contrast enhancement method with RDH for color images

The contrast enhancement with reversible data hiding (CERDH) has become a hot topic in many fields. At present, most schemes applying CERDH methods to color images have the problem of color distortion. To solve this problem, this paper proposes a CERDH method for color images based on constant hue plane. It firstly separates the color images into min, mid and max channels. Then, a novel preprocessing is proposed to maintain the distribution of histogram for original image while vacate room for data hiding. After that, the mid channel is used for data hiding, and min and max channels are hue-preserved to avoid color distortion. Finally, the three channels compose a contrast-enhanced color image. The experimental results show that the proposed method achieves the best enhanced results based on CIEDE2000 compared with the state-of-the-art methods, and color distortion is also greatly reduced.

Underwater low-light enhancement network based on bright channel prior and attention mechanism.

At present, there are some problems in underwater low light image, such as low contrast, blurred details, color distortion. In the process of low illumination image enhancement, there are often problems such as artifacts, loss of edge details and noise amplification in the enhanced image. In this paper, we propose an underwater low-light enhancement algorithm based on U-shaped generative adversarial network, combined with bright channel prior and attention mechanism, to address the problems. For the problems of uneven edges and loss of details that occurred in traditional enhanced images, we propose a two-channel fusion technique for the input channel. Aiming at the problems of brightness, texture and color distortion in enhanced images, we propose a feature extraction technique based on the attention mechanism. For the problems of noise in enhanced output images, we propose a multi-loss function to constrain the network. The method has a wide range of applications in underwater scenes with large depth. This method can be used for target detection or biological species identification in underwater low light environment. Through the enhancement experiment of underwater low light image, the proposed method effectively solves the problems of low contrast, blurred details, color distortion, etc. of underwater low light image. Finally, we performed extensive comparison experiments and completed ablation experiments on the proposed method. The experimental results show that the proposed method is optimal in human visual experience and underwater image quality evaluation index.

A dehazing method for flight view images based on transformer and physical priori.

Aiming at the problems of local dehazing distortion and incomplete global dehazing of existing algorithms in real airborne cockpit environments, a two-stage dehazing method PhysiFormer combining physical a priori with a Transformer oriented flight perspective was proposed. The first stage used synthetic pairwise data to pre-train the dehazing model. First, a pyramid pooling module (PPM) was introduced in the Transformer for multiscale feature extraction to solve the problem of poor recovery of local details, then a global context fusion mechanism was used to enable the model to better perceive global information. Finally, considering that combining the physical a priori needs to rely on the estimation of the atmosphere light, an encoding-decoding structure based on the residual blocks was used to estimate the atmosphere light, which was then used for dehazing through the atmospheric scattering model for dehazing. The second stage used real images combined with physical priori to optimize the model to better fit the real airborne environment. The experimental results show that the proposed method has better naturalness image quality evaluator (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE) indexes and exhibits the best dehazing visual effect in the tests of dense haze, non-uniform haze and real haze images, which effectively improves the problems of color distortion and haze residue.

Dense Residual Fusion and Spatial Local Filtering Low Light Dehazing Algorithm Based on Attention Mechanism

Image dehazing under low-light condition is easy to produce color distortion, patches and artifacts. To adapt to low-light condition, a dense residual fusion and spatial local filtering dehazing algorithm based on attention mechanism is proposed. Firstly, dense residual block is proposed to increase the depth of the neural network, so that the network can learn more advanced feature information. Then, the spatial and channel attention mechanism is introduced to filter and screen the features, so that the network can distinguish the uneven illumination areas and solve the problems of color distortion. The model adopts the method of spatial local filter enhancement to improve the contrast, clarity and visibility of dehazing results. Finally, a learning of joint loss function constraint network is designed to avoid error amplification and learning hybrid degradation of series structure. In the PyTorch environment, the night urban synthetic haze dataset (NHR) is used for testing and compared with the existing dehazing algorithms such as FFANet and GridDehaze. The experimental results show that compared with other dehazing algorithms, proposed method improves PSNR by 8.01‒14.16 dB and increases SSIM by 0.10‒0.36. In addition, proposed method solves the problems of color distortion, plaque and artifact.