Roadbed disease detection is essential for maintaining road functionality.Ground penetrating radar(GPR)enables non-destructive detection without drilling.However,current identification often relies on manual inspectio...Roadbed disease detection is essential for maintaining road functionality.Ground penetrating radar(GPR)enables non-destructive detection without drilling.However,current identification often relies on manual inspection,which requires extensive experience,suffers from low efficiency,and is highly subjective.As the results are presented as radar images,image processing methods can be applied for fast and objective identification.Deep learning-based approaches now offer a robust solution for automated roadbed disease detection.This study proposes an enhanced Faster Region-based Convolutional Neural Networks(R-CNN)framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation(2D-DFT)for frequency-domain feature fusion.A dedicated GPR image dataset comprising 1650 annotated images was constructed and augmented to 6600 images via median filtering,histogram equalization,and binarization.The proposed model segments defect regions,applies binary masking,and fuses frequency-domain features to improve small-target detection under noisy backgrounds.Experimental results show that the improved Faster R-CNN achieves a mean Average Precision(mAP)of 0.92,representing a 0.22 increase over the baseline.Precision improved by 26%while recall remained stable at 87%.The model was further validated on real urban road data,demonstrating robust detection capability even under interference.These findings highlight the potential of combining GPR with deep learning for efficient,non-destructive roadbed health monitoring.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
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
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
The novel method of improving the quality metric of protein microarray image presented in this paper reduces impulse noise by using an adaptive median filter that employs the switching scheme based on local statistics...The novel method of improving the quality metric of protein microarray image presented in this paper reduces impulse noise by using an adaptive median filter that employs the switching scheme based on local statistics characters; and achieves the impulse detection by using the difference between the standard deviation of the pixels within the filter window and the current pixel of concern. It also uses a top-hat filter to correct the background variation. In order to decrease time consumption, the top-hat filter core is cross structure. The experimental results showed that, for a protein microarray image contaminated by impulse noise and with slow background variation, the new method can significantly increase the signal-to-noise ratio, correct the trends in the background, and enhance the flatness of the background and the consistency of the signal intensity.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
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
To address the issue of deteriorated PCB image quality in the quality inspection process due to insufficient or uneven lighting, we proposed an image enhancement fusion algorithm based on different color spaces. First...To address the issue of deteriorated PCB image quality in the quality inspection process due to insufficient or uneven lighting, we proposed an image enhancement fusion algorithm based on different color spaces. Firstly, an improved MSRCR method was employed for brightness enhancement of the original image. Next, the color space of the original image was transformed from RGB to HSV, followed by processing the S-channel image using bilateral filtering and contrast stretching algorithms. The V-channel image was subjected to brightness enhancement using adaptive Gamma and CLAHE algorithms. Subsequently, the processed image was transformed back to the RGB color space from HSV. Finally, the images processed by the two algorithms were fused to create a new RGB image, and color restoration was performed on the fused image. Comparative experiments with other methods indicated that the contrast of the image was optimized, texture features were more abundantly preserved, brightness levels were significantly improved, and color distortion was prevented effectively, thus enhancing the quality of low-lit PCB images.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
In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But...In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.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
A recent study by Nishizawa et al presented significant findings regarding the advantages of next-generation colonoscopes,specifically the CF-XZ1200 and CFEZ1500 models,in enhancing the adenoma and sessile serrated le...A recent study by Nishizawa et al presented significant findings regarding the advantages of next-generation colonoscopes,specifically the CF-XZ1200 and CFEZ1500 models,in enhancing the adenoma and sessile serrated lesion detection rates.As colorectal cancer remains a leading cause of cancer-related mortality globally,the implications of improved detection rates are substantial.This letter advocated the adoption of advanced colonoscopy technology,emphasizing the robust methodology of the study,including propensity score matching,which enhanced the validity of its conclusions.Notable improvements in image quality,facilitated by innovations such as 4 K resolution and texture enhancement imaging,enable endoscopists to identify even the smallest lesions,ultimately leading to improved patient outcomes.Given the compelling evidence presented,it is imperative for healthcare institutions to prioritize the integration of these advanced scopes into routine practice to enhance screening efficacy and reduce the burden of colorectal cancer.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
There is still a dearth of systematic study on picture stitching techniques for the natural tubular structures of intestines,and traditional stitching techniques have a poor application to endoscopic images with deep ...There is still a dearth of systematic study on picture stitching techniques for the natural tubular structures of intestines,and traditional stitching techniques have a poor application to endoscopic images with deep scenes.In order to recreate the intestinal wall in two dimensions,a method is developed.The normalized Laplacian algorithm is used to enhance the image and transform it into polar coordinates according to the characteristics that intestinal images are not obvious and usually arranged in a circle,in order to extract the new image segments of the current image relative to the previous image.The improved weighted fusion algorithm is then used to sequentially splice the segment images.The experimental results demonstrate that the suggested approach can improve image clarity and minimize noise while maintaining the information content of intestinal images.In addition,the method's seamless transition between the final portions of a panoramic image also demonstrates that the stitching trace has been removed.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
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
Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third...Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a designed multi-attention mechanism focuses on important local features during the feature extraction stage. Moreover, a triplet loss function is utilized to learn discriminative hash codes to construct a compact and efficient triplet deep hashing. Finally, upsampling is used to restore the original resolution of the images during retrieval, thereby enabling more accurate matching. To ensure the security of medical image data, a lightweight image encryption method based on frequency domain encryption is designed to encrypt the chest X-ray images. The findings of the experiment indicate that, in comparison to various advanced image retrieval techniques, the suggested approach improves the precision of feature extraction and retrieval using the COVIDx dataset. Additionally, it offers enhanced protection for the confidentiality of medical images stored in cloud settings and demonstrates strong practicality.展开更多
基金supported by the Second Batch of Key Textbook Construction Projects of“14th Five-Year Plan”of Zhejiang Vocational Colleges(SZDJC-2412).
文摘Roadbed disease detection is essential for maintaining road functionality.Ground penetrating radar(GPR)enables non-destructive detection without drilling.However,current identification often relies on manual inspection,which requires extensive experience,suffers from low efficiency,and is highly subjective.As the results are presented as radar images,image processing methods can be applied for fast and objective identification.Deep learning-based approaches now offer a robust solution for automated roadbed disease detection.This study proposes an enhanced Faster Region-based Convolutional Neural Networks(R-CNN)framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation(2D-DFT)for frequency-domain feature fusion.A dedicated GPR image dataset comprising 1650 annotated images was constructed and augmented to 6600 images via median filtering,histogram equalization,and binarization.The proposed model segments defect regions,applies binary masking,and fuses frequency-domain features to improve small-target detection under noisy backgrounds.Experimental results show that the improved Faster R-CNN achieves a mean Average Precision(mAP)of 0.92,representing a 0.22 increase over the baseline.Precision improved by 26%while recall remained stable at 87%.The model was further validated on real urban road data,demonstrating robust detection capability even under interference.These findings highlight the potential of combining GPR with deep learning for efficient,non-destructive roadbed health monitoring.
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.
文摘The novel method of improving the quality metric of protein microarray image presented in this paper reduces impulse noise by using an adaptive median filter that employs the switching scheme based on local statistics characters; and achieves the impulse detection by using the difference between the standard deviation of the pixels within the filter window and the current pixel of concern. It also uses a top-hat filter to correct the background variation. In order to decrease time consumption, the top-hat filter core is cross structure. The experimental results showed that, for a protein microarray image contaminated by impulse noise and with slow background variation, the new method can significantly increase the signal-to-noise ratio, correct the trends in the background, and enhance the flatness of the background and the consistency of the signal intensity.
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.
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To address the issue of deteriorated PCB image quality in the quality inspection process due to insufficient or uneven lighting, we proposed an image enhancement fusion algorithm based on different color spaces. Firstly, an improved MSRCR method was employed for brightness enhancement of the original image. Next, the color space of the original image was transformed from RGB to HSV, followed by processing the S-channel image using bilateral filtering and contrast stretching algorithms. The V-channel image was subjected to brightness enhancement using adaptive Gamma and CLAHE algorithms. Subsequently, the processed image was transformed back to the RGB color space from HSV. Finally, the images processed by the two algorithms were fused to create a new RGB image, and color restoration was performed on the fused image. Comparative experiments with other methods indicated that the contrast of the image was optimized, texture features were more abundantly preserved, brightness levels were significantly improved, and color distortion was prevented effectively, thus enhancing the quality of low-lit PCB images.
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.
文摘In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.
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.
文摘A recent study by Nishizawa et al presented significant findings regarding the advantages of next-generation colonoscopes,specifically the CF-XZ1200 and CFEZ1500 models,in enhancing the adenoma and sessile serrated lesion detection rates.As colorectal cancer remains a leading cause of cancer-related mortality globally,the implications of improved detection rates are substantial.This letter advocated the adoption of advanced colonoscopy technology,emphasizing the robust methodology of the study,including propensity score matching,which enhanced the validity of its conclusions.Notable improvements in image quality,facilitated by innovations such as 4 K resolution and texture enhancement imaging,enable endoscopists to identify even the smallest lesions,ultimately leading to improved patient outcomes.Given the compelling evidence presented,it is imperative for healthcare institutions to prioritize the integration of these advanced scopes into routine practice to enhance screening efficacy and reduce the burden of colorectal cancer.
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the Special Research Fund for the Natural Science Foundation of Chongqing(No.cstc2019jcyjmsxm1351)the Science and Technology Research Project of Chongqing Education Commission(No.KJQN2020006300)。
文摘There is still a dearth of systematic study on picture stitching techniques for the natural tubular structures of intestines,and traditional stitching techniques have a poor application to endoscopic images with deep scenes.In order to recreate the intestinal wall in two dimensions,a method is developed.The normalized Laplacian algorithm is used to enhance the image and transform it into polar coordinates according to the characteristics that intestinal images are not obvious and usually arranged in a circle,in order to extract the new image segments of the current image relative to the previous image.The improved weighted fusion algorithm is then used to sequentially splice the segment images.The experimental results demonstrate that the suggested approach can improve image clarity and minimize noise while maintaining the information content of intestinal images.In addition,the method's seamless transition between the final portions of a panoramic image also demonstrates that the stitching trace has been removed.
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.
基金supported by the NationalNatural Science Foundation of China(No.61862041).
文摘Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a designed multi-attention mechanism focuses on important local features during the feature extraction stage. Moreover, a triplet loss function is utilized to learn discriminative hash codes to construct a compact and efficient triplet deep hashing. Finally, upsampling is used to restore the original resolution of the images during retrieval, thereby enabling more accurate matching. To ensure the security of medical image data, a lightweight image encryption method based on frequency domain encryption is designed to encrypt the chest X-ray images. The findings of the experiment indicate that, in comparison to various advanced image retrieval techniques, the suggested approach improves the precision of feature extraction and retrieval using the COVIDx dataset. Additionally, it offers enhanced protection for the confidentiality of medical images stored in cloud settings and demonstrates strong practicality.
