Multi-scale and Multi-stage Deraining Network with Fourier Space Loss

The goal of rain streak removal is to recover the rain-free background scenes of an image degraded by rain streaks. Most current deep convolutional neural networks methods have achieved dramatic performance. However, these methods still cannot capture the discriminative features to well distinguish the rain streaks and the important image content. To solve this problem, we propose a Multi-scale and Multi-stage deraining network in the end-to-end manner. Specifically, we design a multi-scale rain streak extraction module to capture complex rain streak features across different scales through the multi-scale selection kernel attention mechanism. In addition, multi-stage learning is used to extract deeper feature representations of rain streak and fuse different stages of background information. Furthermore, we introduce a Fourier space loss function to reduce the loss of high-frequency information in the background image and improve the quality of deraining results. Extensive experiments demonstrate that our network performs favorably against the state-of-the-art deraining methods.