Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ign...Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.展开更多
In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of ea...In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.展开更多
Aiming at the scattering and absorption of light in the water body,which causes the problems of color shift,uneven brightness,poor sharpness and missing details in the acquired underwater images,an underwater image en...Aiming at the scattering and absorption of light in the water body,which causes the problems of color shift,uneven brightness,poor sharpness and missing details in the acquired underwater images,an underwater image enhancement algorithm based on IMSRCR and CLAHE-WGIF is proposed.Firstly,the IMSRCR algorithm proposed in this paper is used to process the original underwater image with adaptive color shift correction;secondly,the image is converted to HSV color space,and the segmentation exponential algorithm is used to process the S component to enhance the image saturation;finally,multi-scale Retinex is used to decompose the V component image into detail layer and base layer,and adaptive two-dimensional gamma correction is made to the base layer to adjust the brightness unevenness,while the detail layer is processed by CLAHE-WGIF algorithm to enhance the image contrast and detail information.The experimental results show that our algorithm has some advantages over existing algorithms in both subjective and objective evaluations,and the information entropy of the image is improved by 6.3%on average,and the UIQM and UCIQE indexes are improved by 12.9%and 20.3%on average.展开更多
The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images,which can impede the ever-growing demand for underwater robot operations....The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images,which can impede the ever-growing demand for underwater robot operations.To address these issues,we propose a Multi-Color space Residual Network(MCRNet)for underwater image enhancement.Our method takes advantage of the unique features of color representation in the RGB,HSV,and Lab color spaces.By utilizing the distinct feature representations of images in different color spaces,we can highlight and fuse the most informative features of the three color spaces.Our approach employs a self-attention mechanism in the multi-color space feature fusion module.Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images,particularly in severely degraded scenes.Consequently,our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.展开更多
Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean engineering.Although extensive research has been conducted into underwater image enhanc...Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean engineering.Although extensive research has been conducted into underwater image enhancement,little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes.In this paper,we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters,which effectively removes color casts of a variety of underwater images.A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed,which circumvents the influence of white or bright regions that challenges existing physical model-based methods.To enhance contrast of resultant images,a piece-wise affine transform is applied to the transmission map estimated via background light differential.Finally,with the estimated background light and transmission map,the scene radiance is recovered by addressing an inverse problem of image formation model.Extensive experiments reveal that our results are characterized by natural appearance and genuine color,and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics,which further validates the better robustness and higher generalization ability of our enhancement model.展开更多
Clear,correct imaging is a prerequisite for underwater operations.In real freshwater environment including rivers and lakes,the water bodies are usually turbid and dynamic,which brings extra troubles to quality of ima...Clear,correct imaging is a prerequisite for underwater operations.In real freshwater environment including rivers and lakes,the water bodies are usually turbid and dynamic,which brings extra troubles to quality of imaging due to color deviation and suspended particulate.Most of the existing underwater imaging methods focus on relatively clear underwater environment,it is uncertain that if those methods can work well in turbid and dynamic underwater environments.In this paper,we propose a turbidity-adaptive underwater image enhancement method.To deal with attenuation and scattering of varying degree,the turbidity is detected by the histogram of images.Based on the detection result,different image enhancement strategies are designed to deal with the problem of color deviation and blurring.The proposed method is verified by an underwater image dataset captured in real underwater environment.The result is evaluated by image metrics including structure similarity index measure,underwater color image quality evaluation metric,and speeded-up robust features.Test results exhibit that the method can correct the color deviation and improve the quality of underwater images.展开更多
Images that are taken underwater mostly present color shift with hazy effects due to the special property of water.Underwater image enhancement methods are proposed to handle this issue.However,their enhancement resul...Images that are taken underwater mostly present color shift with hazy effects due to the special property of water.Underwater image enhancement methods are proposed to handle this issue.However,their enhancement results are only evaluated on a small number of underwater images.The lack of a sufficiently large and diverse dataset for efficient evaluation of underwater image enhancement methods provokes the present paper.The present paper proposes an organized method to synthesize diverse underwater images,which can function as a benchmark dataset.The present synthesis is based on the underwater image formation model,which describes the physical degradation process.The indoor RGB-D image dataset is used as the seed for underwater style image generation.The ambient light is simulated based on the statistical mean value of real-world underwater images.Attenuation coefficients for diverse water types are carefully selected.Finally,in total 14490 underwater images of 10 water types are synthesized.Based on the synthesized database,state-of-the-art image enhancement methods are appropriately evaluated.Besides,the large diverse underwater image database is beneficial in the development of learning-based methods.展开更多
With the continuous development of the economy and society,plastic pollution in rivers,lakes,oceans,and other bodies of water is increasingly severe,posing a serious challenge to underwater ecosystems.Effective cleani...With the continuous development of the economy and society,plastic pollution in rivers,lakes,oceans,and other bodies of water is increasingly severe,posing a serious challenge to underwater ecosystems.Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste.However,it often causes significant challenges such as noise interference,low contrast,and blurred textures in underwater optical images.A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed,which combines weighted logarithmic transformations,adaptive gamma correction,improved multi-scale Retinex(MSR)algorithm,and the contrast limited adaptive histogram equalization(CLAHE)algorithm.The proposed algorithm improves brightness,contrast,and color recovery and enhances detail features resulting in better overall image quality.A network framework is proposed in this article based on the YOLOv5 model.MobileViT is used as the backbone of the network framework,detection layer is added to improve the detection capability for small targets,self-attention and mixed-attention modules are introduced to enhance the recognition capability of important features.The cross stage partial(CSP)structure is employed in the spatial pyramid pooling(SPP)section to enrich feature information,and the complete intersection over union(CIOU)loss is replaced with the focal efficient intersection over union(EIOU)loss to accelerate convergence while improving regression accuracy.Experimental results proved that the target recognition algorithm achieved a recognition accuracy of 0.913 and ensured a recognition speed of 45.56 fps/s.Subsequently,Using red,green,blue and depth(RGB-D)camera to construct a system for identifying and locating underwater plastic waste.Experiments were conducted underwater for recognition,localization,and error analysis.The experimental results demonstrate the effectiveness of the proposed method for identifying and locating underwater plastic waste,and it has good localization accuracy.展开更多
Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable,which cause the underwater image of sea cucumbers to be distorted,blurred,and severely attenuated.Therefore,the...Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable,which cause the underwater image of sea cucumbers to be distorted,blurred,and severely attenuated.Therefore,the valuable information from such an image cannot be fully extracted for further processing.To solve the problems mentioned above and improve the quality of the underwater images of sea cucumbers,pre-processing of a sea cucumber image is attracting increasing interest.This paper presents a newmethod based on contrast limited adaptive histogram equalization and wavelet transform(CLAHE-WT)to enhance the sea cucumber image quality.CLAHE was used to process the underwater image for increasing contrast based on the Rayleigh distribution,and WTwas used for de-noising based on a soft threshold.Qualitative analysis indicated that the proposed method exhibited better performance in enhancing the quality and retaining the image details.For quantitative analysis,the test with 120 underwater images showed that for the proposed method,the mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were 49.2098,13.3909,and 6.6815,respectively.The proposed method outperformed three established methods in enhancing the visual quality of sea cucumber underwater gray image.展开更多
基金supported in part by the National Key Research and Development Program of China(2020YFB1313002)the National Natural Science Foundation of China(62276023,U22B2055,62222302,U2013202)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-22-003C1)the Postgraduate Education Reform Project of Henan Province(2021SJGLX260Y)。
文摘Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.
文摘In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.
文摘Aiming at the scattering and absorption of light in the water body,which causes the problems of color shift,uneven brightness,poor sharpness and missing details in the acquired underwater images,an underwater image enhancement algorithm based on IMSRCR and CLAHE-WGIF is proposed.Firstly,the IMSRCR algorithm proposed in this paper is used to process the original underwater image with adaptive color shift correction;secondly,the image is converted to HSV color space,and the segmentation exponential algorithm is used to process the S component to enhance the image saturation;finally,multi-scale Retinex is used to decompose the V component image into detail layer and base layer,and adaptive two-dimensional gamma correction is made to the base layer to adjust the brightness unevenness,while the detail layer is processed by CLAHE-WGIF algorithm to enhance the image contrast and detail information.The experimental results show that our algorithm has some advantages over existing algorithms in both subjective and objective evaluations,and the information entropy of the image is improved by 6.3%on average,and the UIQM and UCIQE indexes are improved by 12.9%and 20.3%on average.
基金supported in part by the National Key R&D Program of China(2022YFB4702300)in part by the National Natural Science Foundation of China(62273097)+3 种基金in part by the Guangdong Basic and Applied Basic Research Foundation,China(2022A1515140044,2019A1515110304,2020A1515110255,and 2021B1515120017)in part by the Research Foundation of Universities of Guangdong Province,China(2019KZDZX1026,2020KCXTD015,and 2021KCXTD083)in part by the Foshan Key Area Technology Research Foundation,China(2120001011009)in part by the Guangdong Philosophy and Social Science Program,China(GD23XTS03).
文摘The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images,which can impede the ever-growing demand for underwater robot operations.To address these issues,we propose a Multi-Color space Residual Network(MCRNet)for underwater image enhancement.Our method takes advantage of the unique features of color representation in the RGB,HSV,and Lab color spaces.By utilizing the distinct feature representations of images in different color spaces,we can highlight and fuse the most informative features of the three color spaces.Our approach employs a self-attention mechanism in the multi-color space feature fusion module.Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images,particularly in severely degraded scenes.Consequently,our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.
基金supported by Higher Education Scientific Research Project of Ningxia(NGY2017009).
文摘Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean engineering.Although extensive research has been conducted into underwater image enhancement,little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes.In this paper,we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters,which effectively removes color casts of a variety of underwater images.A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed,which circumvents the influence of white or bright regions that challenges existing physical model-based methods.To enhance contrast of resultant images,a piece-wise affine transform is applied to the transmission map estimated via background light differential.Finally,with the estimated background light and transmission map,the scene radiance is recovered by addressing an inverse problem of image formation model.Extensive experiments reveal that our results are characterized by natural appearance and genuine color,and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics,which further validates the better robustness and higher generalization ability of our enhancement model.
基金This work was supported by the Guangdong Innovative and Entrepreneurial Research Team Program,China(Grant No.2019ZT08Z780)in part by the Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents,China,in part by the National Key R&D Program of China(Grant No.2017YFC0821200)+1 种基金in part by the Guangdong Basic and Applied Basic Research Foundation,China(Grant No.2021A1515011717)in part by the Space Trusted Computing and Electronic Information Technology Laboratory of BICE,China(Grant No.OBCandETL-2020-06).
文摘Clear,correct imaging is a prerequisite for underwater operations.In real freshwater environment including rivers and lakes,the water bodies are usually turbid and dynamic,which brings extra troubles to quality of imaging due to color deviation and suspended particulate.Most of the existing underwater imaging methods focus on relatively clear underwater environment,it is uncertain that if those methods can work well in turbid and dynamic underwater environments.In this paper,we propose a turbidity-adaptive underwater image enhancement method.To deal with attenuation and scattering of varying degree,the turbidity is detected by the histogram of images.Based on the detection result,different image enhancement strategies are designed to deal with the problem of color deviation and blurring.The proposed method is verified by an underwater image dataset captured in real underwater environment.The result is evaluated by image metrics including structure similarity index measure,underwater color image quality evaluation metric,and speeded-up robust features.Test results exhibit that the method can correct the color deviation and improve the quality of underwater images.
文摘Images that are taken underwater mostly present color shift with hazy effects due to the special property of water.Underwater image enhancement methods are proposed to handle this issue.However,their enhancement results are only evaluated on a small number of underwater images.The lack of a sufficiently large and diverse dataset for efficient evaluation of underwater image enhancement methods provokes the present paper.The present paper proposes an organized method to synthesize diverse underwater images,which can function as a benchmark dataset.The present synthesis is based on the underwater image formation model,which describes the physical degradation process.The indoor RGB-D image dataset is used as the seed for underwater style image generation.The ambient light is simulated based on the statistical mean value of real-world underwater images.Attenuation coefficients for diverse water types are carefully selected.Finally,in total 14490 underwater images of 10 water types are synthesized.Based on the synthesized database,state-of-the-art image enhancement methods are appropriately evaluated.Besides,the large diverse underwater image database is beneficial in the development of learning-based methods.
基金supported by the Foundation of Henan Key Laboratory of Underwater Intelligent Equipment under Grant No.KL02C2105Project of SongShan Laboratory under Grant No.YYJC062022012+2 种基金Training Plan for Young Backbone Teachers in Colleges and Universities in Henan Province under Grant No.2021GGJS077Key Scientific Research Projects of Colleges and Universities in Henan Province under Grant No.22A460022North China University of Water Resources and Electric Power Young Backbone Teacher Training Project under Grant No.2021-125-4.
文摘With the continuous development of the economy and society,plastic pollution in rivers,lakes,oceans,and other bodies of water is increasingly severe,posing a serious challenge to underwater ecosystems.Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste.However,it often causes significant challenges such as noise interference,low contrast,and blurred textures in underwater optical images.A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed,which combines weighted logarithmic transformations,adaptive gamma correction,improved multi-scale Retinex(MSR)algorithm,and the contrast limited adaptive histogram equalization(CLAHE)algorithm.The proposed algorithm improves brightness,contrast,and color recovery and enhances detail features resulting in better overall image quality.A network framework is proposed in this article based on the YOLOv5 model.MobileViT is used as the backbone of the network framework,detection layer is added to improve the detection capability for small targets,self-attention and mixed-attention modules are introduced to enhance the recognition capability of important features.The cross stage partial(CSP)structure is employed in the spatial pyramid pooling(SPP)section to enrich feature information,and the complete intersection over union(CIOU)loss is replaced with the focal efficient intersection over union(EIOU)loss to accelerate convergence while improving regression accuracy.Experimental results proved that the target recognition algorithm achieved a recognition accuracy of 0.913 and ensured a recognition speed of 45.56 fps/s.Subsequently,Using red,green,blue and depth(RGB-D)camera to construct a system for identifying and locating underwater plastic waste.Experiments were conducted underwater for recognition,localization,and error analysis.The experimental results demonstrate the effectiveness of the proposed method for identifying and locating underwater plastic waste,and it has good localization accuracy.
基金supported by the International Science&Technology Cooperation Program of China(2015DFA00090)Special Fund for Agro-scientific Research in the Public Interest(201203017).
文摘Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable,which cause the underwater image of sea cucumbers to be distorted,blurred,and severely attenuated.Therefore,the valuable information from such an image cannot be fully extracted for further processing.To solve the problems mentioned above and improve the quality of the underwater images of sea cucumbers,pre-processing of a sea cucumber image is attracting increasing interest.This paper presents a newmethod based on contrast limited adaptive histogram equalization and wavelet transform(CLAHE-WT)to enhance the sea cucumber image quality.CLAHE was used to process the underwater image for increasing contrast based on the Rayleigh distribution,and WTwas used for de-noising based on a soft threshold.Qualitative analysis indicated that the proposed method exhibited better performance in enhancing the quality and retaining the image details.For quantitative analysis,the test with 120 underwater images showed that for the proposed method,the mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were 49.2098,13.3909,and 6.6815,respectively.The proposed method outperformed three established methods in enhancing the visual quality of sea cucumber underwater gray image.
