Transcription of Dynamic Attentive Graph Learning for Image Restoration
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Dynamic Attentive Graph Learning for Image Restoration
Dynamic Attentive Graph Learning for Image RestorationChong Mou , Jian Zhang , , Zhuoyuan Wu Peking University Shenzhen Graduate School, Shenzhen, China Peng Cheng Laboratory, Shenzhen, self-similarity in natural images has been ver-ified to be an effective prior for Image Restoration . However,most existing deep non-local methods assign a fixed numberof neighbors for each query item, neglecting the dynamicsof non-local correlations. Moreover, the non-local correla-tions are usually based on pixels, prone to be biased due toimage degradation. To rectify these weaknesses, in this pa-per, we propose a Dynamic Attentive Graph Learning model(DAGL) to explore the Dynamic non-local property on patchlevel for Image Restoration . Specifically, we propose an im-proved Graph model to perform patch-wise Graph convo-lution with a Dynamic and adaptive number of neighborsfor each node.
HQ + n, where H is a linear degra-dation matrix, and n represents additive noise [48,55]. Ac-cording to H, IR can be categorized into many subtasks, e.g., denoising, compression artifact reduction, demosaic-ing, super-resolution, compressive sensing [49,53,54,46]. The rise of deep learning has greatly facilitated the de-
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