An Improved Weighted Nuclear Norm Minimization Method for Image Denoising
- 주제(키워드) Image denoising , image gradient , constrained least squares method , low rank matrix approximation , self-similarity , similarity measure , weighted nuclear norm minimization
- 주제(기타) Computer Science, Information Systems
- 주제(기타) Engineering, Electrical & Electronic
- 주제(기타) Telecommunications
- 설명문(일반) [Yang, Hyoseon] Michigan State Univ, Dept Computat Math Sci & Engn, E Lansing, MI 48824 USA; [Park, Yunjin; Yoon, Jungho] Ewha Womans Univ, Dept Math, Seoul 03760, South Korea; [Jeong, Byeongseon] Ewha Womans Univ, Inst Math Sci, Seoul 03760, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 gold
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2019
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000172013
- 본문언어 영어
- Published As https://dx.doi.org/10.1109/ACCESS.2019.2929541
초록/요약
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.
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