COUPLING SPATIAL AND CHANNEL TRANSFORMER FOR SINGLE IMAGE DERAINING

Single image deraining is a fundamental low-level vision task, and has evolved remarkable progress with the deep learning technique. Recently, benefiting from the powerful modeling ability of long-range dependence, transformer as an alternative architecture of the dominant convolutional neural network has demonstrated large margin performance improvement in various high-level vision tasks, and has begun to be applied for low-level vision tasks. The benchmark transformer block captures long dependence via incorporating the self-attention among the spatial points of the learned feature map, and causes heavy computational workload and memory footprint quadratically increased with spatial resolutions, making it impossible to handle high-resolution images. This study proposes a novel spatial and channel coupled Transformer to jointly explore long-range dependence and correlation in both spatial and channel domains, and results in a lightweight deraining transformer model for potentially processing high-resolution images.