An Adaptive Markov Random Field for Structured Compressive Sensing

TitleAn Adaptive Markov Random Field for Structured Compressive Sensing
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsSuwanwimolkul, S., L. Zhang, D. Gong, Z. Zhang, C. Chen, D. C. Ranasinghe, and Q. Shi
JournalIEEE Transactions on Image Processing
Abstract

Abstract—Exploiting intrinsic structures in sparse signals un-derpins 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 study, 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 aximize 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.