Natural image restoration based on multi-scale group sparsity residual constraints

Ning, Wan and Sun, Dong and Gao, Qingwei and Lu, Yixiang and Zhu, De (2023) Natural image restoration based on multi-scale group sparsity residual constraints. Frontiers in Neuroscience, 17. ISSN 1662-453X

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The Group Sparse Representation (GSR) model shows excellent potential in various image restoration tasks. In this study, we propose a novel Multi-Scale Group Sparse Residual Constraint Model (MS-GSRC) which can be applied to various inverse problems, including denoising, inpainting, and compressed sensing (CS). Our new method involves the following three steps: (1) finding similar patches with an overlapping scheme for the input degraded image using a multi-scale strategy, (2) performing a group sparse coding on these patches with low-rank constraints to get an initial representation vector, and (3) under the Bayesian maximum a posteriori (MAP) restoration framework, we adopt an alternating minimization scheme to solve the corresponding equation and reconstruct the target image finally. Simulation experiments demonstrate that our proposed model outperforms in terms of both objective image quality and subjective visual quality compared to several state-of-the-art methods.

Item Type: Article
Subjects: STM Library > Multidisciplinary
Depositing User: Managing Editor
Date Deposited: 08 Nov 2023 08:47
Last Modified: 08 Nov 2023 08:47

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