Traditional patch-based sparse representation modeling of natural images usually suffer
from two problems. First, it has to solve a large-scale optimization problem with high …

As a spotlighted nonlocal image representation model, group sparse representation (GSR)
has demonstrated a great potential in diverse image restoration tasks. Most of the existing …

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity
models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing …

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

Group sparse representation (GSR) has made great strides in image restoration producing
superior performance, realized through employing a powerful mechanism to integrate the …

Sparse representation has achieved great success in various image processing and
computer vision tasks. For image processing, typical patch-based sparse representation …

Recent works on structural sparse representation (SSR), which exploit image nonlocal self-
similarity (NSS) prior by grouping similar patches for processing, have demonstrated …

Sparse representation models code an image patch as a linear combination of a few atoms
chosen out from an over-complete dictionary, and they have shown promising results in …

Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar
patches to construct patch groups, recent studies have revealed that structural sparse …

Sparse coding has achieved a great success in various image processing tasks. However, a
benchmark to measure the sparsity of image patch/group is missing since sparse coding is …