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 …

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

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

Sparse representation based modeling has been successfully used in many image-related
inverse problems such as deblurring, super-resolution and compressive sensing. The heart …

Sparse representation has recently attracted enormous interests in the field of image
restoration. The conventional sparsity-based methods enforce sparse coding on small …

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 representation is proven to be a promising approach to image super-resolution,
where the low-resolution (LR) image is usually modeled as the down-sampled version of its …

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

In image processing, sparse coding has been known to be relevant to both variational and
Bayesian approaches. The regularization parameter in variational image restoration is …