A Single Image Deraining Algorithm Based on Swin Transformer

Abstract

Single image deraining aims to recover the rain-free image from rainy image. Most existing deraining methods based on deep learning do not utilize the global information of rainy image effectively, which makes them lose much detailed and structural information after processing. Focusing on this issue, this paper proposes a single image deraining algorithm based on Swin Transformer. The network mainly includes a shallow features extraction module and a deep features extraction network. The former exploits the context information aggregation module to adapt to the distribution diversity of rain streaks and extracts the shallow features of rainy image. The latter uses Swin Transformer to capture the global information and long-distance dependencies between different pixels, in combination with residual convolution and dense connection to strengthen features learning. Finally, the derained image is obtained through a global residual convolution. In addition, this paper proposes a novel comprehensive loss function that constrains the similarity of image edges and regions synchronously to further improve the quality of derained image. Extensive experimental results show that, compared with the two state-of-the-art methods, MSPFN and MPRNet, the average peak signal-to-noise ratio of derained images of our method increases by 0.19 dB and 2.17 dB, and the average structural similarity increases by 3.433% and 1.412%. At the same time, the model parameters of the proposed network decreases by 84.59% and 34.53%, and the forward propagation time reduces by 21.25% and 26.67%.

Keywords

• computer vision
• single image deraining
• swin transformer
• residual network
• self-attention mechanism
• dilated convolution