Efficient $\ell_q$ Minimization Algorithms for Compressive Sensing Based on Proximity Operator

TL;DR

This paper introduces two fast, scalable algorithms for nonconvex _q-minimization in compressive sensing, improving convergence and performance without smoothing the _q-norm, and outperforming existing methods.

Contribution

The paper develops two novel first-order algorithms incorporating the proximity operator for _q-norms into FISTA and ADMM frameworks, enhancing efficiency and convergence in sparse signal recovery.

Findings

Proposed algorithms are the fastest for _q-minimization.

They outperform existing methods in sparse signal recovery.

Algorithms scale well for large-scale image processing.

Abstract

This paper considers solving the unconstrained $\ell_q$-norm ($0\leq q<1$) regularized least squares ($\ell_q$-LS) problem for recovering sparse signals in compressive sensing. We propose two highly efficient first-order algorithms via incorporating the proximity operator for nonconvex $\ell_q$-norm functions into the fast iterative shrinkage/thresholding (FISTA) and the alternative direction method of multipliers (ADMM) frameworks, respectively. Furthermore, in solving the nonconvex $\ell_q$-LS problem, a sequential minimization strategy is adopted in the new algorithms to gain better global convergence performance. Unlike most existing $\ell_q$-minimization algorithms, the new algorithms solve the $\ell_q$-minimization problem without smoothing (approximating) the $\ell_q$-norm. Meanwhile, the new algorithms scale well for large-scale problems, as often encountered in image processing. We show that the proposed algorithms are the fastest methods in solving the nonconvex $\ell_q$-minimization problem, while offering competent performance in recovering sparse signals and compressible images compared with several state-of-the-art algorithms.