The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adver...The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.展开更多
Low-light image enhancement(LLIE)remains challenging due to underexposure,color distortion,and amplified noise introduced during illumination correction.Existing deep learning–based methods typically apply uniform en...Low-light image enhancement(LLIE)remains challenging due to underexposure,color distortion,and amplified noise introduced during illumination correction.Existing deep learning–based methods typically apply uniform enhancement across the entire image,which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction.To overcome these limitations,we propose a Semantic-Guided Visual Mamba Network(SGVMNet)that unifies semantic reasoning,state-space modeling,and mixture-of-experts routing for adaptive illumination correction.SGVMNet comprises three key components:(1)a semantic modulation module(SMM)that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant(LLaVA)and Contrastive Language–Image Pretraining(CLIP)—and injects them hierarchically into the feature stream;(2)aMixture-of-Experts State-Space Feature EnhancementModule(MoE-SSMFEM)that dynamically selects informative channels and activates specialized state-space experts for efficient global–local illumination modeling;and(3)a Text-Guided Mixture Mamba Block(TGMB)that fuses semantic priors and visual features through bidirectional state propagation.Experimental results demonstrate that on the low-light(LOL)dataset,SGVMNet outperforms other state-of-the-art methods in both quantitative and qualitative evaluations,and it also maintains low computational complexity with fast inference speed.On LOLv2-Syn,SGVMNet achieves 26.512 dB PSNR and 0.935 SSIM,outperforming RetinexFormer by 0.61 dB.On LOLv1,SGVMNet attains 26.50 dB PSNR and 0.863 SSIM.Furthermore,experiments on multiple unpaired real-world datasets further validate the superiority of SGVMNet,showing that the model not only exhibits strong cross-scene generalization ability but also effectively preserves semantic consistency and visual naturalness.展开更多
Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater imag...Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater image enhancement methods,although they can improve the image quality to some extent,often lead to problems such as detail loss and edge blurring.To address these problems,we propose FENet,an efficient underwater image enhancement method.FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra,respectively.Then,a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part,thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part.Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation.For this reason,we propose a multi-stage residual feature aggregation module,which focuses on detail extraction and effectively avoids information loss caused by global enhancement.Finally,we combine the edge guidance strategy to further enhance the edge details of the image.Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)meth...Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.展开更多
In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,th...In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis.The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image,followed by image segmentation using a quadtree method.To improve the accuracy and robustness of atmospheric light estimation,the algorithm incorporates a genetic algorithm to optimize the quadtree-based estimation of atmospheric light regions.Additionally,this method employs an adaptive window adjustment mechanism to derive the dark channel prior image,which is subsequently refined using morphological operations and guided filtering.The final enhanced image is reconstructed through the hazy image degradation model.Extensive experimental evaluations across multiple datasets verify the superiority of the designed framework,achieving a peak signal-to-noise ratio(PSNR)of 17.09 and a structural similarity index(SSIM)of 0.74.These results indicate that the proposed algorithm not only effectively enhances image contrast and brightness but also outperforms traditional methods in terms of subjective and objective evaluation metrics.展开更多
The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV imag...The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.展开更多
To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illuminat...To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illumination is processed by contrast-limited adaptive histogram equalization(CLAHE),adaptive complementary gamma function(ACG),and adaptive detail preserving S-curve(ADPS),respectively,to obtain three components.Then,the fusion-relevant features,exposure,and color contrast are selected as the weight maps.Subsequently,these components and weight maps are fused through multi-scale to generate enhanced illumination.Finally,the enhanced images are obtained by multiplying the enhanced illumination and reflectance.Compared with existing approaches,this proposed method achieves an average increase of 0.81%and 2.89%in the structural similarity index measurement(SSIM)and peak signal-to-noise ratio(PSNR),and a decrease of 6.17%and 32.61%in the natural image quality evaluator(NIQE)and gradient magnitude similarity deviation(GMSD),respectively.展开更多
Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenari...Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenarios,including fluctuating noise levels and unpredictable environmental elements,these techniques do not fully resolve these challenges.We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture,merging the Convolutional Block Attention Module(CBAM)with the Transformer.Our model is capable of detecting more prominent features across both channel and spatial domains.We have conducted extensive experiments across several datasets,namely LOLv1,LOLv2-real,and LOLv2-sync.The results show that our approach surpasses other methods when evaluated against critical metrics such as Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM).Moreover,we have visually assessed images enhanced by various techniques and utilized visual metrics like LPIPS for comparison,and the experimental data clearly demonstrate that our approach excels visually over other methods as well.展开更多
Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevita...Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevitably,influencing the quality of enhanced images.To alleviate this problem,a low-light image enhancement model called RetinexNet model based on Retinex theory was proposed in this study.The model was composed of an image decomposition module and a brightness enhancement module.In the decomposition module,a convolutional block attention module(CBAM)was incorporated to enhance feature representation capacity of the network,focusing on crucial features and suppressing irrelevant ones.A multifeature fusion denoising module was designed within the brightness enhancement module,circumventing the issue of feature loss during downsampling.The proposed model outperforms the existing algorithms in terms of PSNR and SSIM metrics on the publicly available datasets LOL and MIT-Adobe FiveK,as well as gives superior results in terms of NIQE metrics on the publicly available dataset LIME.展开更多
The phenomenon of attenuation and scattering of light propagating in water leads to such problems as color deviation and blur in underwater imaging.These problems bring great challenges to the subsequent feature match...The phenomenon of attenuation and scattering of light propagating in water leads to such problems as color deviation and blur in underwater imaging.These problems bring great challenges to the subsequent feature matching,target recognition and other tasks.Therefore,this paper proposes an underwater image enhancement method by double compensation with comparative adjustment or edge reinforcement.The experiments have found that the proposed method has good underwater color image quality evaluation(UCIQE)value,underwater image quality measures(UIQM)value,and the number of feature matching points.This demonstrates that the proposed method has good color correction ability for underwater images with different attenuation levels,where the processed images have more details suitable for feature matching.展开更多
Health monitoring of underwater concrete facility systems is important in civil engineering. Unlike conventional manual inspection techniques, digital image processing offers a more convenient and effective approach, ...Health monitoring of underwater concrete facility systems is important in civil engineering. Unlike conventional manual inspection techniques, digital image processing offers a more convenient and effective approach, becoming an indispensable tool for structural inspection. Cracks, which are pervasive defects, are a central focus of structural deterioration research. However, the complexity of the marine environment poses challenges to underwater visibility.In this study, the underwater environment under controlled laboratory conditions is replicated, where varying turbidity and illumination conditions and images of concrete cracks are captured. An approach combining a defogging algorithm with guided and fast guided filtering techniques is proposed to enhance both natural underwater images and crack images captured through experimental photography. When applied to turbid crack images captured under two different suspension conditions, the method increases the information entropy(IE) by 32.92% and 17.92% and the underwater color image quality evaluation(UCIQE) by 35.76% and 18.36%, respectively. These results demonstrate its efficiency in enhancing image definition. The findings of this study could significantly impact the practical applications of image visualization and evaluation for underwater concrete cracks.展开更多
Within the domain of low-level vision,enhancing low-light images and removing sand-dust from single images are both critical tasks.These challenges are particularly pronounced in real-world applications such as autono...Within the domain of low-level vision,enhancing low-light images and removing sand-dust from single images are both critical tasks.These challenges are particularly pronounced in real-world applications such as autonomous driving,surveillance systems,and remote sensing,where adverse lighting and environmental conditions often degrade image quality.Various neural network models,including MLPs,CNNs,GANs,and Transformers,have been proposed to tackle these challenges,with the Vision KAN models showing particular promise.However,existing models,including the Vision KAN models use deterministic neural networks that do not address the uncertainties inherent in these processes.To overcome this,we introduce the Uncertainty-Aware Kolmogorov-Arnold Network(UAKAN),a novel structure that integrates KAN with uncertainty estimation.Our approach uniquely employs Tokenized KANs for sampling within a U-Net architecture’s encoder and decoder layers,enhancing the network’s ability to learn complex representations.Furthermore,for aleatoric uncertainty,we propose an uncertainty coupling certainty module that couples uncertainty distribution learning and residual learning in a feature fusion manner.For epistemic uncertainty,we propose a feature selection mechanism for spatial and pixel dimension uncertainty modeling,which captures and models uncertainty by learning the uncertainty contained between feature maps.Notably,our uncertainty-aware framework enables the model to produce both high-quality enhanced images and reliable uncertainty maps,which are crucial for downstream applications requiring confidence estimation.Through comparative and ablation studies on our synthetic SLLIE6K dataset,designed for low-light enhancement and sand-dust removal,we validate the effectiveness and theoretical robustness of our methodology.展开更多
Low-light images often have defects such as low visibility,low contrast,high noise,and high color distortion compared with well-exposed images.If the low-light region of an image is enhanced directly,the noise will in...Low-light images often have defects such as low visibility,low contrast,high noise,and high color distortion compared with well-exposed images.If the low-light region of an image is enhanced directly,the noise will inevitably blur the whole image.Besides,according to the retina-and-cortex(retinex)theory of color vision,the reflectivity of different image regions may differ,limiting the enhancement performance of applying uniform operations to the entire image.Therefore,we design a Hierarchical Flow Learning(HFL)framework,which consists of a Hierarchical Image Network(HIN)and a normalized invertible Flow Learning Network(FLN).HIN can extract hierarchical structural features from low-light images,while FLN maps the distribution of normally exposed images to a Gaussian distribution using the learned hierarchical features of low-light images.In subsequent testing,the reversibility of FLN allows inferring and obtaining enhanced low-light images.Specifically,the HIN extracts as much image information as possible from three scales,local,regional,and global,using a Triple-branch Hierarchical Fusion Module(THFM)and a Dual-Dconv Cross Fusion Module(DCFM).The THFM aggregates regional and global features to enhance the overall brightness and quality of low-light images by perceiving and extracting more structure information,whereas the DCFM uses the properties of the activation function and local features to enhance images at the pixel-level to reduce noise and improve contrast.In addition,in this paper,the model was trained using a negative log-likelihood loss function.Qualitative and quantitative experimental results demonstrate that our HFL can better handle many quality degradation types in low-light images compared with state-of-the-art solutions.The HFL model enhances low-light images with better visibility,less noise,and improved contrast,suitable for practical scenarios such as autonomous driving,medical imaging,and nighttime surveillance.Outperforming them by PSNR=27.26 dB,SSIM=0.93,and LPIPS=0.10 on benchmark dataset LOL-v1.The source code of HFL is available at https://github.com/Smile-QT/HFL-for-LIE.展开更多
Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approac...Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.展开更多
A wavelet-based local and global feature fusion network(LAGN)is proposed for low-light image enhancement,aiming to enhance image details and restore colors in dark areas.This study focuses on addressing three key issu...A wavelet-based local and global feature fusion network(LAGN)is proposed for low-light image enhancement,aiming to enhance image details and restore colors in dark areas.This study focuses on addressing three key issues in low-light image enhancement:Enhancing low-light images using LAGN to preserve image details and colors;extracting image edge information via wavelet transform to enhance image details;and extracting local and global features of images through convolutional neural networks and Transformer to improve image contrast.Comparisons with state-of-the-art methods on two datasets verify that LAGN achieves the best performance in terms of details,brightness,and contrast.展开更多
Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algori...Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.展开更多
Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images...Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled Retine (DDR) method, an unsupervised approach that integrates denoising priors into a Retinex-based training framework. By explicitly incorporating denoising, the DDR method effectively addresses the challenges of noise and artifacts in low-light images, thereby enhancing the performance of the Retinex framework. The model achieved a PSNR of 19.82 dB on the LOL dataset, which is comparable to the performance of supervised methods. Furthermore, by applying knowledge distillation, the DDR method optimizes the model for real-time processing of low-light images, achieving a processing speed of 199.7 fps without incurring additional computational costs. While the DDR method has demonstrated superior performance in terms of image quality and processing speed, there is still room for improvement in terms of robustness across different color spaces and under highly resource-constrained conditions. Future research will focus on enhancing the model’s generalizability and adaptability to address these challenges. Our rigorous testing on public datasets further substantiates the DDR method’s state-of-the-art performance in both image quality and processing speed.展开更多
Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning...Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels as distance increases. In illumination restoration, we used Unet++ with multi-level skip connections to better integrate semantic information at different scales. The designed Illumination Fusion Dual Self-Attention (IF-DSA) module embeds multi-scale dilated convolutions to achieve spatial self-attention. This module captures long-range spatial semantic relationships within acceptable computational complexity. Experimental results on the Low-Light (LOL) dataset show that Retexformer+ outperforms other State-Of-The-Art (SOTA) methods in both quantitative and qualitative evaluations, with the computational complexity increased to an acceptable 51.63 G FLOPS. On the LOL_v1 dataset, RetinexFormer+ shows an increase of 1.15 in Peak Signal-to-Noise Ratio (PSNR) and a decrease of 0.39 in Root Mean Square Error (RMSE). On the LOL_v2_real dataset, the PSNR increases by 0.42 and the RMSE decreases by 0.18. Experimental results on the Exdark dataset show that Retexformer+ can effectively enhance real-scene images and maintain their semantic information.展开更多
In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization tec...In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization technique and automatically determines the color spectra of geophysical maps. Colors can be properly distributed and visual effects and resolution can be enhanced by the method. The other method is based on the modified Radon transform and gradient calculation and is used to detect and enhance linear features in gravity and magnetic images. The method facilites the detection of line segments in the transform domain. Tests with synthetic images and real data show the methods to be effective in feature enhancement.展开更多
文摘The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.
文摘Low-light image enhancement(LLIE)remains challenging due to underexposure,color distortion,and amplified noise introduced during illumination correction.Existing deep learning–based methods typically apply uniform enhancement across the entire image,which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction.To overcome these limitations,we propose a Semantic-Guided Visual Mamba Network(SGVMNet)that unifies semantic reasoning,state-space modeling,and mixture-of-experts routing for adaptive illumination correction.SGVMNet comprises three key components:(1)a semantic modulation module(SMM)that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant(LLaVA)and Contrastive Language–Image Pretraining(CLIP)—and injects them hierarchically into the feature stream;(2)aMixture-of-Experts State-Space Feature EnhancementModule(MoE-SSMFEM)that dynamically selects informative channels and activates specialized state-space experts for efficient global–local illumination modeling;and(3)a Text-Guided Mixture Mamba Block(TGMB)that fuses semantic priors and visual features through bidirectional state propagation.Experimental results demonstrate that on the low-light(LOL)dataset,SGVMNet outperforms other state-of-the-art methods in both quantitative and qualitative evaluations,and it also maintains low computational complexity with fast inference speed.On LOLv2-Syn,SGVMNet achieves 26.512 dB PSNR and 0.935 SSIM,outperforming RetinexFormer by 0.61 dB.On LOLv1,SGVMNet attains 26.50 dB PSNR and 0.863 SSIM.Furthermore,experiments on multiple unpaired real-world datasets further validate the superiority of SGVMNet,showing that the model not only exhibits strong cross-scene generalization ability but also effectively preserves semantic consistency and visual naturalness.
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901].
文摘Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering,color distortion,and detail blurring,limiting their application performance.Existing underwater image enhancement methods,although they can improve the image quality to some extent,often lead to problems such as detail loss and edge blurring.To address these problems,we propose FENet,an efficient underwater image enhancement method.FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra,respectively.Then,a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part,thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part.Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation.For this reason,we propose a multi-stage residual feature aggregation module,which focuses on detail extraction and effectively avoids information loss caused by global enhancement.Finally,we combine the edge guidance strategy to further enhance the edge details of the image.Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金supported by theHubei Provincial Technology Innovation Special Project and the Natural Science Foundation of Hubei Province under Grants 2023BEB024,2024AFC066,respectively.
文摘Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.
基金supported by the Natural Science Foundation of Shandong Province(nos.ZR2023MF047,ZR2024MA055 and ZR2023QF139)the Enterprise Commissioned Project(nos.2024HX104 and 2024HX140)+1 种基金the China University Industry-University-Research Innovation Foundation(nos.2021ZYA11003 and 2021ITA05032)the Science and Technology Plan for Youth Innovation of Shandong's Universities(no.2019KJN012).
文摘In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis.The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image,followed by image segmentation using a quadtree method.To improve the accuracy and robustness of atmospheric light estimation,the algorithm incorporates a genetic algorithm to optimize the quadtree-based estimation of atmospheric light regions.Additionally,this method employs an adaptive window adjustment mechanism to derive the dark channel prior image,which is subsequently refined using morphological operations and guided filtering.The final enhanced image is reconstructed through the hazy image degradation model.Extensive experimental evaluations across multiple datasets verify the superiority of the designed framework,achieving a peak signal-to-noise ratio(PSNR)of 17.09 and a structural similarity index(SSIM)of 0.74.These results indicate that the proposed algorithm not only effectively enhances image contrast and brightness but also outperforms traditional methods in terms of subjective and objective evaluation metrics.
基金supported by the National Natural Science Foundation of China(Nos.62201454 and 62306235)the Xi’an Science and Technology Program of Xi’an Science and Technology Bureau(No.23SFSF0004)。
文摘The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.
基金supported by the National Key R&D Program of China(No.2022YFB3205101)NSAF(No.U2230116)。
文摘To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illumination is processed by contrast-limited adaptive histogram equalization(CLAHE),adaptive complementary gamma function(ACG),and adaptive detail preserving S-curve(ADPS),respectively,to obtain three components.Then,the fusion-relevant features,exposure,and color contrast are selected as the weight maps.Subsequently,these components and weight maps are fused through multi-scale to generate enhanced illumination.Finally,the enhanced images are obtained by multiplying the enhanced illumination and reflectance.Compared with existing approaches,this proposed method achieves an average increase of 0.81%and 2.89%in the structural similarity index measurement(SSIM)and peak signal-to-noise ratio(PSNR),and a decrease of 6.17%and 32.61%in the natural image quality evaluator(NIQE)and gradient magnitude similarity deviation(GMSD),respectively.
文摘Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenarios,including fluctuating noise levels and unpredictable environmental elements,these techniques do not fully resolve these challenges.We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture,merging the Convolutional Block Attention Module(CBAM)with the Transformer.Our model is capable of detecting more prominent features across both channel and spatial domains.We have conducted extensive experiments across several datasets,namely LOLv1,LOLv2-real,and LOLv2-sync.The results show that our approach surpasses other methods when evaluated against critical metrics such as Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM).Moreover,we have visually assessed images enhanced by various techniques and utilized visual metrics like LPIPS for comparison,and the experimental data clearly demonstrate that our approach excels visually over other methods as well.
文摘Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevitably,influencing the quality of enhanced images.To alleviate this problem,a low-light image enhancement model called RetinexNet model based on Retinex theory was proposed in this study.The model was composed of an image decomposition module and a brightness enhancement module.In the decomposition module,a convolutional block attention module(CBAM)was incorporated to enhance feature representation capacity of the network,focusing on crucial features and suppressing irrelevant ones.A multifeature fusion denoising module was designed within the brightness enhancement module,circumventing the issue of feature loss during downsampling.The proposed model outperforms the existing algorithms in terms of PSNR and SSIM metrics on the publicly available datasets LOL and MIT-Adobe FiveK,as well as gives superior results in terms of NIQE metrics on the publicly available dataset LIME.
基金supported by the National Natural Science Foundation of China(Nos.62372100 and 6237118)。
文摘The phenomenon of attenuation and scattering of light propagating in water leads to such problems as color deviation and blur in underwater imaging.These problems bring great challenges to the subsequent feature matching,target recognition and other tasks.Therefore,this paper proposes an underwater image enhancement method by double compensation with comparative adjustment or edge reinforcement.The experiments have found that the proposed method has good underwater color image quality evaluation(UCIQE)value,underwater image quality measures(UIQM)value,and the number of feature matching points.This demonstrates that the proposed method has good color correction ability for underwater images with different attenuation levels,where the processed images have more details suitable for feature matching.
基金financially supported by the National Natural Science Foundation of China (Grant No. 52175245)the Natural Science Foundation of Hubei Province (Grant No. 2021CFB462)。
文摘Health monitoring of underwater concrete facility systems is important in civil engineering. Unlike conventional manual inspection techniques, digital image processing offers a more convenient and effective approach, becoming an indispensable tool for structural inspection. Cracks, which are pervasive defects, are a central focus of structural deterioration research. However, the complexity of the marine environment poses challenges to underwater visibility.In this study, the underwater environment under controlled laboratory conditions is replicated, where varying turbidity and illumination conditions and images of concrete cracks are captured. An approach combining a defogging algorithm with guided and fast guided filtering techniques is proposed to enhance both natural underwater images and crack images captured through experimental photography. When applied to turbid crack images captured under two different suspension conditions, the method increases the information entropy(IE) by 32.92% and 17.92% and the underwater color image quality evaluation(UCIQE) by 35.76% and 18.36%, respectively. These results demonstrate its efficiency in enhancing image definition. The findings of this study could significantly impact the practical applications of image visualization and evaluation for underwater concrete cracks.
基金supported by National Key R&D Program of China(2023YFB2504400).
文摘Within the domain of low-level vision,enhancing low-light images and removing sand-dust from single images are both critical tasks.These challenges are particularly pronounced in real-world applications such as autonomous driving,surveillance systems,and remote sensing,where adverse lighting and environmental conditions often degrade image quality.Various neural network models,including MLPs,CNNs,GANs,and Transformers,have been proposed to tackle these challenges,with the Vision KAN models showing particular promise.However,existing models,including the Vision KAN models use deterministic neural networks that do not address the uncertainties inherent in these processes.To overcome this,we introduce the Uncertainty-Aware Kolmogorov-Arnold Network(UAKAN),a novel structure that integrates KAN with uncertainty estimation.Our approach uniquely employs Tokenized KANs for sampling within a U-Net architecture’s encoder and decoder layers,enhancing the network’s ability to learn complex representations.Furthermore,for aleatoric uncertainty,we propose an uncertainty coupling certainty module that couples uncertainty distribution learning and residual learning in a feature fusion manner.For epistemic uncertainty,we propose a feature selection mechanism for spatial and pixel dimension uncertainty modeling,which captures and models uncertainty by learning the uncertainty contained between feature maps.Notably,our uncertainty-aware framework enables the model to produce both high-quality enhanced images and reliable uncertainty maps,which are crucial for downstream applications requiring confidence estimation.Through comparative and ablation studies on our synthetic SLLIE6K dataset,designed for low-light enhancement and sand-dust removal,we validate the effectiveness and theoretical robustness of our methodology.
基金supported by the National Natural Science Foundation of China(Grant Nos.61971078,61501070)the Scientific Research Foundation of Chongqing University of Technology(Grant No.0121230236)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJ202301165).
文摘Low-light images often have defects such as low visibility,low contrast,high noise,and high color distortion compared with well-exposed images.If the low-light region of an image is enhanced directly,the noise will inevitably blur the whole image.Besides,according to the retina-and-cortex(retinex)theory of color vision,the reflectivity of different image regions may differ,limiting the enhancement performance of applying uniform operations to the entire image.Therefore,we design a Hierarchical Flow Learning(HFL)framework,which consists of a Hierarchical Image Network(HIN)and a normalized invertible Flow Learning Network(FLN).HIN can extract hierarchical structural features from low-light images,while FLN maps the distribution of normally exposed images to a Gaussian distribution using the learned hierarchical features of low-light images.In subsequent testing,the reversibility of FLN allows inferring and obtaining enhanced low-light images.Specifically,the HIN extracts as much image information as possible from three scales,local,regional,and global,using a Triple-branch Hierarchical Fusion Module(THFM)and a Dual-Dconv Cross Fusion Module(DCFM).The THFM aggregates regional and global features to enhance the overall brightness and quality of low-light images by perceiving and extracting more structure information,whereas the DCFM uses the properties of the activation function and local features to enhance images at the pixel-level to reduce noise and improve contrast.In addition,in this paper,the model was trained using a negative log-likelihood loss function.Qualitative and quantitative experimental results demonstrate that our HFL can better handle many quality degradation types in low-light images compared with state-of-the-art solutions.The HFL model enhances low-light images with better visibility,less noise,and improved contrast,suitable for practical scenarios such as autonomous driving,medical imaging,and nighttime surveillance.Outperforming them by PSNR=27.26 dB,SSIM=0.93,and LPIPS=0.10 on benchmark dataset LOL-v1.The source code of HFL is available at https://github.com/Smile-QT/HFL-for-LIE.
基金Graduate Student Innovation Projects of Beijing University of Civil Engineering and Architecture(No.PG2024121)。
文摘Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.
文摘A wavelet-based local and global feature fusion network(LAGN)is proposed for low-light image enhancement,aiming to enhance image details and restore colors in dark areas.This study focuses on addressing three key issues in low-light image enhancement:Enhancing low-light images using LAGN to preserve image details and colors;extracting image edge information via wavelet transform to enhance image details;and extracting local and global features of images through convolutional neural networks and Transformer to improve image contrast.Comparisons with state-of-the-art methods on two datasets verify that LAGN achieves the best performance in terms of details,brightness,and contrast.
基金supported by the Research on theKey Technology of Damage Identification Method of Dam Concrete Structure based on Transformer Image Processing(242102521031)the project Research on Situational Awareness and Behavior Anomaly Prediction of Social Media Based on Multimodal Time Series Graph(232102520004)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B520019).
文摘Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.
基金support by the Guangxi Natural Science Foundation(Grant No.2024GXNSFAA010484)the NationalNatural Science Foundation of China(No.62466013),this work has been made possible.
文摘Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled Retine (DDR) method, an unsupervised approach that integrates denoising priors into a Retinex-based training framework. By explicitly incorporating denoising, the DDR method effectively addresses the challenges of noise and artifacts in low-light images, thereby enhancing the performance of the Retinex framework. The model achieved a PSNR of 19.82 dB on the LOL dataset, which is comparable to the performance of supervised methods. Furthermore, by applying knowledge distillation, the DDR method optimizes the model for real-time processing of low-light images, achieving a processing speed of 199.7 fps without incurring additional computational costs. While the DDR method has demonstrated superior performance in terms of image quality and processing speed, there is still room for improvement in terms of robustness across different color spaces and under highly resource-constrained conditions. Future research will focus on enhancing the model’s generalizability and adaptability to address these challenges. Our rigorous testing on public datasets further substantiates the DDR method’s state-of-the-art performance in both image quality and processing speed.
基金supported by the Key Laboratory of Forensic Science and Technology at College of Sichuan Province(2023YB04).
文摘Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels as distance increases. In illumination restoration, we used Unet++ with multi-level skip connections to better integrate semantic information at different scales. The designed Illumination Fusion Dual Self-Attention (IF-DSA) module embeds multi-scale dilated convolutions to achieve spatial self-attention. This module captures long-range spatial semantic relationships within acceptable computational complexity. Experimental results on the Low-Light (LOL) dataset show that Retexformer+ outperforms other State-Of-The-Art (SOTA) methods in both quantitative and qualitative evaluations, with the computational complexity increased to an acceptable 51.63 G FLOPS. On the LOL_v1 dataset, RetinexFormer+ shows an increase of 1.15 in Peak Signal-to-Noise Ratio (PSNR) and a decrease of 0.39 in Root Mean Square Error (RMSE). On the LOL_v2_real dataset, the PSNR increases by 0.42 and the RMSE decreases by 0.18. Experimental results on the Exdark dataset show that Retexformer+ can effectively enhance real-scene images and maintain their semantic information.
基金This work is supported by the research project (grant No. G20000467) of the Institute of Geology and Geophysics, CAS and bythe China Postdoctoral Science Foundation (No. 2004036083).
文摘In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization technique and automatically determines the color spectra of geophysical maps. Colors can be properly distributed and visual effects and resolution can be enhanced by the method. The other method is based on the modified Radon transform and gradient calculation and is used to detect and enhance linear features in gravity and magnetic images. The method facilites the detection of line segments in the transform domain. Tests with synthetic images and real data show the methods to be effective in feature enhancement.
