Graph Attention Neural Network for Image Restoration
Chong Mou, Jian Zhang · 2021
Self-similarity underpins modern non-local attention mechanism, which has been verified to be an effective prior for image restoration. However, most existing non-local attention restorers are implemented based on pixels, which tend to be biased due to image degeneration. Furthermore, most non-local methods for image restoration are restricted to construct fully-connected correlations in a regular Euclidean space so that all features within the search region have to participate in the feature aggregation process, no matter how similar the key feature is to the query feature. To rectify these weaknesses, in this paper, we propose a novel graph attention network for image restoration, dubbed GATIR, which establishes the non-local attention based on feature patches and utilizes the graph convolution to perform feature aggregation selectively in a non-Euclidean space. Experimental results demonstrate that our GATIR can achieve state-of-the-art performance on synthetic image denoising, real image denoising, image demosaicing, and compression artifact reduction tasks.