基于像素级水平的通道自适应水下图像增强算法OA北大核心CSTPCD

The existing algorithms based on deep learning enhance underwater images in high-dimensional features by encoding and decoding,without considering the channel difference degradation characteristics of underwater images,resulting in generally poor enhancement effects.To solve this problem,this paper proposes an underwater image pixel-level channel enhancement algorithm based on deep learning,which enhances the underwater image at the pixel level into three channels:R,G and B.The algorithm is divided into four stages,and the whole enhancement process is completed through four stages of sub-channel feature extraction.The network first fixes the color channel of the context by enhancing the local and global semantics of the network and optimizing the channel attenuation.Secondly,the spatial and channel features are aggregated by an attention mechanism,and irrelevant color localization jump information is suppressed.Then,the adaptive features are adjusted by optimizing the attention mechanism.Finally,in order to improve the ability of color shift correction,a color shift correction module is proposed.In the fourth stage,a color shift adjustment module is used to further adjust the color shift problem of the image.Experimental results show that compared with other algorithms on the UIEB dataset and EUVP dataset,the proposed algorithm improves the PSNR index by 14.35%,the SSIM index by 5.8%,the UIQM index by 3.2%,and the UCIQE index by 13.7%,and has the best subjective effect.