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Deep neural networks (DNN) have achieved great suc-cess in image restoration. However, most DNN methods are designed as a black box, lacking transparency and inter-pretability. Although some methods are proposed to combine traditional optimization algorithms with DNN, they usually demand pre-defined degradation processes or hand-crafted assumptions, making it difficult to deal with complex and real-world applications. In this paper, we propose a Deep Generalized Unfolding Network (DGUNet) for image restoration. Concretely, without loss of interpretability, we integrate a gradient estimation strategy into the gradi-ent descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to deal with complex and real-world image degradation. In addition, we design inter-stage in-formation pathways across proximal mapping in different PGD iterations to rectify the intrinsic information loss in most deep unfolding networks (DUN) through a multi-scale and spatial-adaptive way. By integrating the flexible gradi-ent descent and informative proximal mapping, we unfold the iterative PGD algorithm into a trainable DNN. Exten-sive experiments on various image restoration tasks demon-strate the superiority of our method in terms of state-of-the-art performance, interpretability, and generalizability. The source code is available at github.com/MC-E/DGUNet.
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Chong Mou
Qian Wang
Jian Zhang
Peng Cheng Laboratory
Peking University Shenzhen Hospital
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Mou et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d8bcae17a1cc0598d180aa — DOI: https://doi.org/10.1109/cvpr52688.2022.01688
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