A Non-Local Enhanced Network for Image Restoration

Abstract

Non-local modules have been widely studied in image restoration (IR) tasks since they can learn long-range dependencies to enhance local features. However, most existing non-local modules still focus on extracting long-range dependencies within a single image or feature map. On the other hand, most IR methods simply employ a single type of non-local module in the network. A combination of various types of non-local modules to enhance local features can be more effective. In this paper, we propose a batch-wise non-local module to explore richer non-local dependencies within images. Furthermore, we combine various non-local extractors (different attention modules) with the proposed batch-wise non-local module as the Enhanced Batch-wise Non-local Attentive module (EBNA). Besides exploring richer non-local information, we build the Non-local and Local Information extracting Block (NLIB), in which we combine the EBNA with DEformable-Convolution Block (DECB) to utilize richer non-local and adaptive local information. Finally, We embed the NLIB within a U-net-like structure and build the Non-local Enhanced Network (NLENet). Extensive experiments on synthetic image denoising, real image denoising, JPEG artifacts removal, and real image super resolution tasks demonstrate that our proposed network achieves state-ofthe-art performance on several IR benchmark datasets.