l 0 Sparsity for Image Denoising with Local and Global Priors
Joint Authors
Yu, Mei
Gao, Xiaoni
Wang, Jianrong
Wei, Jianguo
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-11-04
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
We propose a l0 sparsity based approach to remove additive white Gaussian noise from a given image.
To achieve this goal, we combine the local prior and global prior together to recover the noise-free values of pixels.
The local prior depends on the neighborhood relationships of a search window to help maintain edges and smoothness.
The global prior is generated from a hierarchical l0 sparse representation to help eliminate the redundant information and preserve the global consistency.
In addition, to make the correlations between pixels more meaningful, we adopt Principle Component Analysis to measure the similarities, which can be both propitious to reduce the computational complexity and improve the accuracies.
Experiments on the benchmark image set show that the proposed approach can achieve superior performance to the state-of-the-art approaches both in accuracy and perception in removing the zero-mean additive white Gaussian noise.
American Psychological Association (APA)
Gao, Xiaoni& Yu, Mei& Wang, Jianrong& Wei, Jianguo. 2015. l 0 Sparsity for Image Denoising with Local and Global Priors. Advances in Multimedia،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1052636
Modern Language Association (MLA)
Gao, Xiaoni…[et al.]. l 0 Sparsity for Image Denoising with Local and Global Priors. Advances in Multimedia No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1052636
American Medical Association (AMA)
Gao, Xiaoni& Yu, Mei& Wang, Jianrong& Wei, Jianguo. l 0 Sparsity for Image Denoising with Local and Global Priors. Advances in Multimedia. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1052636
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1052636