Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing NSS-based sparsity models are either too restrictive, e.g. , JS enforces the sparse codes to share the same support, or...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 30; pp. 5223 - 5238
Main Authors: Zha, Zhiyuan, Wen, Bihan, Yuan, Xin, Zhou, Jiantao, Zhu, Ce
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
adaptive parameter adjustment
Algorithms
alternating minimization
Coding
Dictionaries
Group sparse coding
Image coding
Image denoising
Image restoration
low-rank regularized group sparse coding
Mathematical models
Minimization
Noise reduction
Optimization
Self-similarity
Sparse matrices
Sparsity
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing NSS-based sparsity models are either too restrictive, e.g. , JS enforces the sparse codes to share the same support, or too general, e.g. , GSC imposes only plain sparsity on the group coefficients, which limit their effectiveness for modeling real images. In this paper, we propose a novel NSS-based sparsity model, namely, low-rank regularized group sparse coding (LR-GSC) , to bridge the gap between the popular GSC and JS. The proposed LR-GSC model simultaneously exploits the sparsity and low-rankness of the dictionary-domain coefficients for each group of similar patches. An alternating minimization with an adaptive adjusted parameter strategy is developed to solve the proposed optimization problem for different image restoration tasks, including image denoising, image deblocking, image inpainting, and image compressive sensing. Extensive experimental results demonstrate that the proposed LR-GSC algorithm outperforms many popular or state-of-the-art methods in terms of objective and perceptual metrics.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2021.3078329