An adaptive markov random field for structured compressive sensing
Date
2019
Authors
Suwanwimolkul, S.
Zhang, L.
Gong, D.
Zhang, Z.
Chen, C.
Ranasinghe, D.C.
Qinfeng Shi, J.
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Advisors
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Journal article
Citation
IEEE Transactions on Image Processing, 2019; 28(3):1556-1570
Statement of Responsibility
Suwichaya Suwanwimolkul, Lei Zhang, Dong Gong, Zhen Zhang, Chao Chen, Damith C. Ranasinghe and Javen Qinfeng Shi
Conference Name
Abstract
Exploiting intrinsic structures in sparse signals underpin the recent progress in compressive sensing (CS). The key for exploiting such structures is to achieve two desirable properties: generality (i.e., the ability to fit a wide range of signals with diverse structures) and adaptability (i.e., being adaptive to a specific signal). Most existing approaches, however, often only achieve one of these two properties. In this paper, we propose a novel adaptive Markov random field sparsity prior for CS, which not only is able to capture a broad range of sparsity structures, but also can adapt to each sparse signal through refining the parameters of the sparsity prior with respect to the compressed measurements. To maximize the adaptability, we also propose a new sparse signal estimation, where the sparse signals, support, noise, and signal parameter estimation are unified into a variational optimization problem, which can be effectively solved with an alternative minimization scheme. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed method in recovery accuracy, noise tolerance, and runtime.
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© 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.