Multi-Scale Channel Transformer Network for Single Image Deraining
Yuto Namba, Xian‐Hua Han · 2022
Single image deraining is a very challenging task, as it requires not only restoring the spatial details and high contextual structures of the images, but also removing multiple layers of rain with varying degrees of blurring and resolutions. Recently, due to the powerful modeling capability of long-dependency, transformer-based models have manifested superior performance for high-level vision tasks, and have begun to be applied for low-level vision tasks such as various image restoration applications. However, its computational complexity increases quadratically with spatial resolutions, making it impossible to apply it to high-resolution images. In this study, we propose a novel Channel Transformer, which performs self-attention in the channel direction instead of the spatial direction. Specifically, we first incorporate multiple channel transformer blocks into a multi-scale architecture to extract multi-scale contexts and exploit channel long-dependence, and then learn a coarse estimation of the rain-free image. Finally, an original-resolution CNN-based module is employed to refine the coarse estimation via leveraging the previously learned multi-scale contexts. Experiments on several benchmark datasets demonstrate its superiority over the state-of-the-art methods.