An efficient non-convex total variation approach for image deblurring and denoising

Jingjing Liu, Ruijie Ma, Xiaoyang Zeng, Wanquan Liu, Mingyu Wang and Hui Chen

Applied Mathematics and Computation, 2021, vol. 397, issue C

Abstract: Total variation (TV) is broadly utilized in image processing because it is able to preserve sharp edges and object boundaries, which are usually the most important parts of an image. Recently, the non-convex functions such as the smoothly clipped absolute deviation, the minimax concave penalty, the capped ℓ1-norm penalty and the ℓp quasi-norm with 0Keywords: Total variation (TV); Image deblurring and denoising; Non-convex regularization; Optimizing minimization; Alternating direction method of multiplier (ADMM) (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300321000254
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:397:y:2021:i:c:s0096300321000254

DOI: 10.1016/j.amc.2021.125977

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().