An Adaptive Markov Random Field for Structured Compressive Sensing

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., bei...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2019-03, Vol.28 (3), p.1556-1570
Main Authors: Suwanwimolkul, Suwichaya, Zhang, Lei, Gong, Dong, Zhang, Zhen, Chen, Chao, Ranasinghe, Damith C., Qinfeng Shi, Javen
Format: Article
Language:English
Subjects:
Adaptation models
Biomedical measurement
Compressed sensing
Estimation
Fields (mathematics)
Graphical models
Markov processes
Noise tolerance
Parameter estimation
probabilistic graphical models
Probabilistic logic
sparse representation
Sparsity
Structured compressive sensing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2018.2878294