Reconstruction with prior support information and non-Gaussian constraints

TL;DR

This paper introduces a new compressed sensing model, $\,\omega$-BPDQ$_p$, that uses prior support information and non-Gaussian constraints to improve reconstruction robustness, especially for quantized or non-Gaussian noise.

Contribution

The paper proposes the $\,\omega$-BPDQ$_p$ model, incorporating weights and non-Gaussian norms, and establishes theoretical guarantees linking RIP$_{p,q}$ and $\,\omega$-RNSP$_{p,q}$ for stable reconstruction.

Findings

Weighted basis pursuit dequantization improves reconstruction quality.

RIP$_{p,q}$ and $\,\omega$-RNSP$_{p,q}$ properties ensure stability and robustness.

Numerical experiments validate the theoretical advantages of the proposed method.

Abstract

In this study, we introduce a novel model, termed the Weighted Basis Pursuit Dequantization ($\omega$-BPDQ$_p$), which incorporates prior support information by assigning weights on the $\ell_1$ norm in the $\ell_1$ minimization process and replaces the $\ell_2$ norm with the $\ell_p$ norm in the constraint. This adjustment addresses cases where noise deviates from a Gaussian distribution, such as quantized errors, which are common in practice. We demonstrate that Restricted Isometry Property (RIP$_{p,q}$) and Weighted Robust Null Space Property ($\omega$-RNSP$_{p,q}$) ensure stable and robust reconstruction within $\omega$-BPDQ$_p$, with the added observation that standard Gaussian random matrices satisfy these properties with high probability. Moreover, we establish a relationship between RIP$_{p,q}$ and $\omega$-RNSP$_{p,q}$ that RIP$_{p,q}$ implies $\omega$-RNSP$_{p,q}$. Additionally, numerical experiments confirm that the incorporation of weights and the non-Gaussian constraint results in improved reconstruction quality.