Prior versions of reversible data hiding with contrast enhancement(RDHCE)algorithms strongly focused on enhancing the contrast of grayscale images.However,RDHCE has recently witnessed a rise in contrast enhance-ment a...Prior versions of reversible data hiding with contrast enhancement(RDHCE)algorithms strongly focused on enhancing the contrast of grayscale images.However,RDHCE has recently witnessed a rise in contrast enhance-ment algorithms concentrating on color images.This paper implies a method for color images that uses the RGB(red,green,and blue)color model and is based on bi-histogram shifting and image adjustment.Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final enhanced image.Images are first divided into three channels-R,G,and B-and the Max,Med,and Min channels are then determined from these.Before histogram shifting,some calculations are done to determine how many iterations there will be for each channel.The images are adjusted to improve visual quality in the enhanced images after data has been embedded in each channel.The experimental results show that the enhanced images produced by the proposed method are qualitatively and aesthetically superior to those produced by some earlier methods,and their quality was assessed using PSNR,SSIM,RCE,RMBE,and CIEDE2000.The embedding rate obtained by the suggested method is acceptable.展开更多
Poor illumination greatly affects the quality of obtained images.In this paper,a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement.DEANet combines the ...Poor illumination greatly affects the quality of obtained images.In this paper,a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement.DEANet combines the frequency and content information of images and is divided into three subnetworks:decomposition,enhancement,and adjustment networks,which perform image decomposition;denoising,contrast enhancement,and detail preservation;and image adjustment and generation,respectively.The model is trained on the public LOL dataset,and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant No.61662039in part by the Jiangxi Key Natural Science Foundation under No.20192ACBL20031+1 种基金in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST)under Grant No.2019r070in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Prior versions of reversible data hiding with contrast enhancement(RDHCE)algorithms strongly focused on enhancing the contrast of grayscale images.However,RDHCE has recently witnessed a rise in contrast enhance-ment algorithms concentrating on color images.This paper implies a method for color images that uses the RGB(red,green,and blue)color model and is based on bi-histogram shifting and image adjustment.Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final enhanced image.Images are first divided into three channels-R,G,and B-and the Max,Med,and Min channels are then determined from these.Before histogram shifting,some calculations are done to determine how many iterations there will be for each channel.The images are adjusted to improve visual quality in the enhanced images after data has been embedded in each channel.The experimental results show that the enhanced images produced by the proposed method are qualitatively and aesthetically superior to those produced by some earlier methods,and their quality was assessed using PSNR,SSIM,RCE,RMBE,and CIEDE2000.The embedding rate obtained by the suggested method is acceptable.
基金This work was supported by the Shanghai Aerospace Science and Technology Innovation Fund(No.SAST2019-048)the Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology(BNRist)(No.BNR2019TD01022).
文摘Poor illumination greatly affects the quality of obtained images.In this paper,a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement.DEANet combines the frequency and content information of images and is divided into three subnetworks:decomposition,enhancement,and adjustment networks,which perform image decomposition;denoising,contrast enhancement,and detail preservation;and image adjustment and generation,respectively.The model is trained on the public LOL dataset,and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.
