Research on single image rain removal algorithm based on progressive recurrent network

Following the rise of deep learning, neural networks are increasingly used in the field of image rain removal. However, most of the rain removal models proposed so far have problems of poor rain removal quality and low efficiency. Based on PReNet, this paper conducts improvement research from the aspects of lightweight and multi-scale feature extraction, proposes the multi-scale progressive recurrent network MsPRN. The main work and contributions are as follows: Use a progressive cyclic network architecture to significantly reduce the amount of parameters and achieve lightweight models through recursive loops and parameter reuse; Propose the Multi-cascade Progressive Atrous Convolution module (McPAC), which uses atrous convolution and multi-cascade channel separation to lightweightly extract deep feature information and reduce rain streak residue; It is proposed to use the squeeze-and-excitation (SE) module to selectively enhance or suppress the propagation of feature channel information, so as to accurately restore the image background details. Experimental results on the Rain100H, Rain100L and Rain12 synthetic data sets show that MsPRN can achieve better rain removal effects and performance while keeping the model lightweight.