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An Improved Weighted Nuclear Norm Minimization Method for Image Denoising

Title
An Improved Weighted Nuclear Norm Minimization Method for Image Denoising
Authors
Yang, HyoseonPark, YunjinYoon, JunghoJeong, Byeongseon
Ewha Authors
윤정호정병선
SCOPUS Author ID
윤정호scopus; 정병선scopus
Issue Date
2019
Journal Title
IEEE ACCESS
ISSN
2169-3536JCR Link
Citation
IEEE ACCESS vol. 7, pp. 97919 - 97927
Keywords
Image denoisingimage gradientconstrained least squares methodlow rank matrix approximationself-similaritysimilarity measureweighted nuclear norm minimization
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
Patch-based low rank matrix approximation has shown great potential in image denoising. Among state-of-the-art methods in this topic, the weighted nuclear norm minimization (WNNM) has been attracting significant attention due to its competitive denoising performance. For each local patch in an image, the WNNM method groups nonlocal similar patches by block matching to formulate a low-rank matrix. However, the WNNM often chooses irrelevant patches such that it may lose fine details of the image, resulting in undesirable artifacts in the final reconstruction. In this regards, this paper aims to provide a denoising algorithm which further improves the performance of the WNNM method. For this purpose, we develop a new nonlocal similarity measure by exploiting both pixel intensities and gradients and present a filter that enhances edge information in a patch to improve the performance of low rank approximation. The experimental results on widely used test images demonstrate that the proposed denoising algorithm performs better than other state-of-the-art denoising algorithms in terms of PSNR and SSIM indices as well as visual quality.
DOI
10.1109/ACCESS.2019.2929541
Appears in Collections:
자연과학대학 > 수학전공 > Journal papers
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