Single‐image deraining algorithm based on multi‐stage recurrent network

• Published in: IET image processing 2024-02, Vol.18 (3), p.650-663
• Main Authors: Li, Chen, Li, Xueting, Guo, Yecai, Li, Jia
• Format: Article
• Language: English
• Subjects: deep rain characteristics; dilated convolution; multi‐scale; multi‐stage; recursion
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/ipr2.12975

The damage caused by the rain streak attached to the rainy image to the background seriously affects the analysis of image information and subsequent research. Aiming at the problem that the current single image rain removal methods cannot extract the deep rain streak features, a single image deraining algorithm based on multi‐stage recurrent network is proposed, which is committed to separating the rain streak layer and the background layer. The network consists of three stages, each stage is mainly composed of upper and lower scale dilation blocks, the upper scale dilation block includes multi‐scale dilated convolution and scale fusion, the lower scale dilation block is set the same, and the stages are connected using improved gated recurrent units to facilitate parameter sharing between them. The experimental results show that the proposed algorithm outperforms the comparative algorithm in both quantitative indicators and qualitative analysis on synthetic and real datasets, and the restored image retains more detailed information. Aiming at the problem that the current single image rain removal methods cannot extract the deep rain streak features, a single image deraining algorithm based on multi‐stage recursive network is proposed. The experimental results show that the proposed algorithm is superior to the contrast algorithm in quantitative indicators and qualitative analysis in both synthetic and real datasets, and the restored image retains more details information.