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 …

Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …

Blind image deblurring is a challenging problem in computer vision, which aims to restore
both the blur kernel and the latent sharp image from only a blurry observation. Inspired by …

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

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 presents, for the first time, a unified blind method for multi-image super-resolution
(MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution …

This paper proposes a new algorithm to address blind image super-resolution (SR) by
fusing multiple low-resolution (LR) blurred images to render a high-resolution (HR) image …

In various learning-based image restoration tasks, such as image denoising and image
super-resolution, the degradation representations were widely used to model the …

Many image restoration algorithms in recent years are based on patch processing. The core
idea is to decompose the target image into fully overlapping patches, restore each of them …