An Adaptive Markov Random Field for Structured Compressive Sensing

TL;DR

This paper introduces an adaptive Markov random field prior for compressive sensing that captures diverse sparsity structures and adapts to individual signals, improving recovery accuracy and noise robustness.

Contribution

It proposes a novel adaptive sparsity prior and a unified variational optimization framework for structured compressive sensing.

Findings

Improves recovery accuracy on real-world datasets.

Enhances noise tolerance in signal reconstruction.

Reduces runtime compared to existing methods.

Abstract

Exploiting intrinsic structures in sparse signals underpins the recent progress in compressive sensing (CS). The key for exploiting such structures is to achieve two desirable properties: generality (\ie, the ability to fit a wide range of signals with diverse structures) and adaptability (\ie, 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 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.