Sparse signal/image recovery is a challenging topic that has captured a great interest during
the last decades. To address the ill-posedness of the related inverse problem, regularization …

Sparse optimization in overcomplete frames has been widely applied in recent years to ill-
conditioned inverse problems. In particular, analysis-based sparse optimization consists of …

Sparse signal/image recovery is a challenging topic that has captured a great interest during
the last decades, especially in the biomedical field. Many techniques generally try to …

Recently, a lot of attention has been given to penalized least squares problem formulations
for sparse signal reconstruction in the presence of noise. The penalty is responsible for …

Sparse signal reconstruction, as the main link of compressive sensing (CS) theory, has
attracted extensive attention in recent years. The essence of sparse signal reconstruction is …

Sparse regularization has been receiving an increasing interest in the literature. Two main
difficulties are encountered when performing sparse regularization. The first one is how to fix …

This paper presents an adaptive support-driven Bayesian reweighted algorithm based on
shrinkage thresholding, for large-scale sparse signal recovery. The proposed algorithm is …

In this paper, we consider the L 1/L 2 minimization for sparse recovery and study its
relationship with the L 1-αL 2 model. Based on this relationship, we propose three numerical …

Sparse Bayesian learning (SBL) with its self-regulation and uncertainty estimation features,
has become a popular topic in the field of sparse signal recovery. However, it is a …

Recently we proposed an efficient technique based on analysis-based sparsity in tight
frames to restore images affected by shift-invariant blur and additive white Gaussian noise …