RestorNet: An efficient network for multiple degradation image restoration

Abstract

Even though Single Degradation Image Restoration (SDIR) has made significant progress and achieved remarkable performance, Multiple Degradation Image Restoration (MDIR) remains a long-term and arduous challenge to achieve the similar levels of success. To further improve the performance and efficiency of MDIR, we propose a novel MDIR method named RestorNet, which comprises an unsupervised degradation encoder for the learning of multi-scale degradation representations and a Multi-scale Degradation-assisted Restoration Module (MDRM) for image reconstruction. Our RestorNet aims to remove noise, rain, and haze in a unified network from the following three aspects. Firstly, to better distinguish among different degradations and learn the corruption information more accurately, we introduce a degradation-specific contrastive loss based on contrastive learning. Next, we develop a multi-scale degradation representation learning method to improve preservation of the spatial structure and distribution of inputs, and to extract multi-scale information to satisfy the diverse requirements of restoring different degraded images. Finally, to make a more reasonable use of degradation representation, we present a novel semi-guided strategy for effective feature transformation, where the multi-scale degradation representations are only incorporated into the MDRM encoder. For image denoising, deraining, and dehazing, by integrating the approaches above, RestorNet not only outperforms the recent state-of-the-art MDIR algorithms with lower computational complexity, but also achieves impressive performance in SDIR. Extensive experiments demonstrate the effectiveness and superiority of our proposed method.