Single Image Deraining via Two Stages: Bilateral Learning and Feature Decoupling

Single image deraining is an important technique in the field of computer vision. Existing deraining methods remove rain streaks from an image by recovering a clear image directly from a rainy image or by learning the residual components. However, there is a mixed correlation between rain streaks and background information, and it is difficult to remove rain streaks directly from rain images. In this work, to improve the performance and efficiency of image deraining, we propose a novel two-stage rain removal network. In the first stage, we first extract rain-related features in low-resolution paths and map them into bilateral space. Then, affine curves are fitted in the bilateral space. Finally, a slicing operation is performed on the data-dependent lookups in the bilateral grid to separate the rain traces from the background information with less computational effort. Then, in the second stage we design a progressive separation module (PSM) to further decouple the mixing correlation between the background and rain-scar layers in a progressive coupled representation learning manner. Tested on several synthetic rainfall datasets as well as real natural scene images, qualitative and quantitative results show that our proposed method achieves better performance with lower computational resources.