Image Restoration Based on Gradual Reweighted Regularization and Low Rank prior
Author
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-22
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Digital restoration of image with missing data is a basic need for visual communication and industrial applications.
In this paper, making full use of priors of low rank and nonlocal self-similarity a gradual reweighted regularization is proposed for matrix completion and image restoration.
Sparsity-promoting regularization produces much sparser representation of grouped nonlocal similar blocks of image by solving a nonconvex minimization problem.
Moreover, an alternation direction method of multipliers algorithm is developed to speed up iterative solving of the above problem.
Image block classification further enhances the adaptivity of the proposed method.
Experiments on simulated matrix and natural image show that the proposed method obtains better image restoration results, where most lost information is reorcovered and few artifacts are produced.
American Psychological Association (APA)
Wang, Fengling. 2020. Image Restoration Based on Gradual Reweighted Regularization and Low Rank prior. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1202188
Modern Language Association (MLA)
Wang, Fengling. Image Restoration Based on Gradual Reweighted Regularization and Low Rank prior. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1202188
American Medical Association (AMA)
Wang, Fengling. Image Restoration Based on Gradual Reweighted Regularization and Low Rank prior. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1202188
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202188