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Optimizing Shrinkage Curves and Application in Image Denoising
Joint Authors
Deng, Hongyao
Song, Xiuli
Tao, Jinsong
Zhu, Qingxin
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
A shrinkage curve optimization is proposed for weighted nuclear norm minimization and is adapted to image denoising.
The proposed optimization method employs a penalty function utilizing the difference between a latent matrix and its observation and uses odd polynomials to shrink the singular values of the observation matrix.
As a result, the coefficients of polynomial characterize the shrinkage operator fully.
Furthermore, the Frobenius norm of the penalty function is converted into the corresponding spectral norm, and thus the parameter optimization problem can be easily solved by using off-and-shelf plain least-squares.
In the practical application, the proposed denoising method does not work on the whole image at once, but rather a series of matrix termed Rank-Ordered Similar Matrix (ROSM).
Simulation results on 256 noisy images demonstrate the effectiveness of the proposed algorithms.
American Psychological Association (APA)
Deng, Hongyao& Zhu, Qingxin& Tao, Jinsong& Song, Xiuli. 2017. Optimizing Shrinkage Curves and Application in Image Denoising. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1190574
Modern Language Association (MLA)
Deng, Hongyao…[et al.]. Optimizing Shrinkage Curves and Application in Image Denoising. Mathematical Problems in Engineering No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1190574
American Medical Association (AMA)
Deng, Hongyao& Zhu, Qingxin& Tao, Jinsong& Song, Xiuli. Optimizing Shrinkage Curves and Application in Image Denoising. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1190574
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190574