Sparse approximate solutions to linear equations are classically obtained via L1 norm
regularized least squares, but this method often underestimates the true solution. As an …

This book revolves around the question of designing a matrix D∈ Rm× n called dictionary,
such that to obtain good sparse representations y≈ Dx for a class of signals y∈ Rm given …

Abstract The ${{\ell}}^{0} $ minimization of compressed sensing is often relaxed to
${{\ell}}^{1} $, which yields easy computation using the shrinkage map** known as soft …

Total variation denoising is a nonlinear filtering method well suited for the estimation of
piecewise-constant signals observed in additive white Gaussian noise. The method is …

Low-light photography conditions degrade image quality. This study proposes a novel
Retinex-based low-light enhancement method to correctly decompose an input image into …

To assist the clinical diagnosis and treatment of neurological diseases that cause speech
dysarthria such as Parkinson's disease (PD), it is of paramount importance to craft robust …

We consider the iterative shrinkage/thresholding algorithm (ISTA) applied to a cost function
composed of a data fidelity term and a penalty term. The penalty is nonconvex but the …

Image segmentation is of great importance in image processing. In this paper, we propose a
two-stage image segmentation strategy based on the nonconvex ℓ 2− ℓ p approximation of …

The calculation of a sparse approximate solution to a linear system of equations is often
performed using either L1-norm regularization and convex optimization or nonconvex …

Hyperspectral anomaly detection aims at distinguishing targets of interest from the
background without prior knowledge. Although low-rank representation (LRR)-based …