The image rain removal method based on multi-stage progressive processing

Aiming at the problems of incomplete rain pattern removal and texture information loss in the existing image rain removal methods, this paper proposes a multi-stage progressive image rain removal algorithm, which can simultaneously fuse the features of the upper and lower stages and greatly improve the performance of the rain removal algorithm. The rain removal network model consists of three stages. In the first two stages, the improved U-Net coder-decoder structure is used to learn multi-scale context information, and the efficient channel attention network (ECANet) is used for feature extraction, which can reduce the parameters of the network model. In the third stage of becoming lighter, parallel attention subnet (PASNet) is added, which can generate high-resolution features while learning contextual information and spatial details, and can better preserve the output details of images. At the same time, supervised attention module (SAM) is introduced to strengthen feature learning. The experimental results show that the PSNR is 29.37 dB and SSIM is 0.88 on the data set Rain100H; The PSNR is 32.50 dB and SSIM is 0.93 on Test1200, which verifies the effectiveness of the proposed method in the task of image rain removal.