PerNet: Progressive and Efficient All-in-One Image-Restoration Lightweight Network

Abstract

The existing image-restoration methods are only effective for specific degradation tasks, but the type of image degradation in practical applications is unknown, and mismatch between the model and the actual degradation will lead to performance decline. Attention mechanisms play an important role in image-restoration tasks; however, it is difficult for existing attention mechanisms to effectively utilize the continuous correlation information of image noise. In order to solve these problems, we propose a Progressive and Efficient All-in-one Image Restoration Lightweight Network (PerNet). The network consists of a Plug-and-Play Efficient Local Attention Module (PPELAM). The PPELAM is composed of multiple Efficient Local Attention Units (ELAUs) and PPELAM can effectively use the global information and horizontal and vertical correlation of image degradation features in space, so as to reduce information loss and have a small number of parameters. PerNet is able to learn the degradation properties of images very well, which allows us to reach an advanced level in image-restoration tasks. Experiments show that PerNet has excellent results for typical restoration tasks (image deraining, image dehazing, image desnowing and underwater image enhancement), and the excellent performance of ELAU combined with Transformer in the ablation experiment chapter further proves the high efficiency of ELAU.