Paper Info
Title
Multi-Scale Patches Based Image Denoising Using Weighted Nuclear Norm Minimisation
Abstract
As a prior knowledge, non-local self-similarity (NSS) has been widely utilised in ill-posed problems. Actually, similar textures appear not only in a single scale, but also in different scales. Unlike most existing patch-based methods that only explore NSS in the same scale, a multi-scale patches based image denoising algorithm is proposed in this study. The authors have designed a multi-scale strategy to expand the search space of block-matching, which will increase the probability of finding more similar patches. After that, the weighted nuclear norm minimisation (WNNM) algorithm is employed to reveal latent clean patches. With the join of the multi-scale framework, the performance of WNNM can be improved. The proposed algorithm can be used to solve NSS-based image restoration tasks. In this study, mainly image denoising is studied, and its effectiveness is derived through experiments on widely used test images.
Year
DOI
Venue
2020
10.1049/iet-ipr.2019.1654
IET IMAGE PROCESSING
Keywords
DocType
Volume
image restoration, image denoising, minimisation, image texture, weighted nuclear norm minimisation algorithm, latent clean patches, multiscale framework, NSS-based image restoration tasks, widely used test images, multiscale patches, nonlocal self-similarity, ill-posed problems, similar textures, single scale, existing patch-based methods, image denoising algorithm, multiscale strategy
Journal
14
Issue
ISSN
Citations 
13
1751-9659
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yuli Fu120029.90
Junwei Xu211.36
Youjun Xiang342.09
Zhen Chen433.79
Tao Zhu502.37
Lei Cai601.01
Weihong He700.34