# Efficient and Robust Recovery of Sparse Signal and Image Using Generalized Nonconvex Regularization

## TL;DR

This paper introduces a robust sparse signal and image recovery method using generalized nonconvex regularization and an $ ext{L}_1$-loss, demonstrating improved accuracy and scalability over existing algorithms.

## Contribution

It proposes a novel ADMM-based algorithm with smoothing for nonconvex regularization and $ ext{L}_1$-loss, ensuring convergence and enhanced recovery performance.

## Key findings

- Outperforms state-of-the-art algorithms in numerical experiments.
- Effective for large-scale sparse signal and image recovery.
- Provides convergence conditions for the proposed algorithm.

## Abstract

This work addresses the robust reconstruction problem of a sparse signal from compressed measurements. We propose a robust formulation for sparse reconstruction which employs the $\ell_1$-norm as the loss function for the residual error and utilizes a generalized nonconvex penalty for sparsity inducing. The $\ell_1$-loss is less sensitive to outliers in the measurements than the popular $\ell_2$-loss, while the nonconvex penalty has the capability of ameliorating the bias problem of the popular convex LASSO penalty and thus can yield more accurate recovery. To solve this nonconvex and nonsmooth minimization formulation efficiently, we propose a first-order algorithm based on alternating direction method of multipliers (ADMM). A smoothing strategy on the $\ell_1$-loss function has been used in deriving the new algorithm to make it convergent. Further, a sufficient condition for the convergence of the new algorithm has been provided for generalized nonconvex regularization. In comparison with several state-of-the-art algorithms, the new algorithm showed better performance in numerical experiments in recovering sparse signals and compressible images. The new algorithm scales well for large-scale problems, as often encountered in image processing.