Adaptive Sparse Norm and Nonlocal Total Variation Methods for Image Smoothing
Joint Authors
Liu, Qiegen
Xiong, Biao
Zhang, Minghui
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-12-28
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
In computer vision and graphics, it is challenging to decompose various texture/structure patterns from input images.
It is well recognized that how edges are defined and how this prior information guides smoothing are two keys in determining the quality of image smoothing.
While many different approaches have been reported in the literature, sparse norm and nonlocal schemes are two promising tools.
In this study, by integrating a texture measure as the spatially varying data-fidelity/smooth-penalty weight into the sparse norm and nonlocal total variation models, two new methods are presented for feature/structure-preserving filtering.
The first one is a generalized relative total variation (i.e., GRTV) method, which improves the contrast-preserving and edge stiffness-enhancing capabilities of the RTV by extending the range of the penalty function’s norm from 1 to [0, 1].
The other one is a nonlocal version of generalized RTV (i.e., NLGRTV) for which the key idea is to use a modified texture-measure as spatially varying penalty weight and to replace the local candidate pixels with the nonlocal set in the smooth-penalty term.
It is shown that NLGRTV substantially improves the performance of decomposition for regions with faint pixel-boundary.
American Psychological Association (APA)
Liu, Qiegen& Xiong, Biao& Zhang, Minghui. 2014. Adaptive Sparse Norm and Nonlocal Total Variation Methods for Image Smoothing. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-18.
https://search.emarefa.net/detail/BIM-1044263
Modern Language Association (MLA)
Liu, Qiegen…[et al.]. Adaptive Sparse Norm and Nonlocal Total Variation Methods for Image Smoothing. Mathematical Problems in Engineering No. 2014 (2014), pp.1-18.
https://search.emarefa.net/detail/BIM-1044263
American Medical Association (AMA)
Liu, Qiegen& Xiong, Biao& Zhang, Minghui. Adaptive Sparse Norm and Nonlocal Total Variation Methods for Image Smoothing. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-18.
https://search.emarefa.net/detail/BIM-1044263
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1044263