At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …

Non-local self-similarity in natural images has been verified to be an effective prior for image
restoration. However, most existing deep non-local methods assign a fixed number of …

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 …

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

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 …

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

Local and non-local attention-based methods have been well studied in various image
restoration tasks while leading to promising performance. However, most of the existing …

Blind image deblurring is a long standing challenging problem in image processing and low-
level vision. Recently, sophisticated priors such as dark channel prior, extreme channel …

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

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 …