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 …

We propose a novel formulation for relaxed analysis-based sparsity in multiple dictionaries
as a general type of prior for images, and apply it for Bayesian estimation in image …

In this letter, we address sparse signal recovery in a Bayesian framework where sparsity is
enforced on reconstruction coefficients via probabilistic priors. In particular, we focus on the …

Accurate signal recovery or image reconstruction from indirect and possibly undersampled
data is a topic of considerable interest; for example, the literature in the recent field of …

This work focuses on several optimization problems involved in recovery of sparse solutions
of linear inverse problems. Such problems appear in many fields including image and signal …

As a powerful statistical image modeling technique, sparse representation has been
successfully used in various image restoration applications. The success of sparse …

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 …

This paper proposes a novel sparse representation model called centralized sparse
representation (CSR) for image restoration tasks. In order for faithful image reconstruction, it …

Ill-posed linear inverse problems (ILIP), such as restoration and reconstruction, are a core
topic of signal/image processing. A standard formulation for dealing with ILIP consists in a …

This paper proposes a novel algorithmic framework to solve image restoration problems
under sparsity assumptions. As usual, the reconstructed image is the minimum of an …