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An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising
Joint Authors
Hu, Changhua
Zhang, Qi
Yang, Xiaogang
He, Hua-Feng
He, Chuan
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-10
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
We propose an adaptive total generalized variation (TGV) based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising.
The variable splitting (VS) and the classical augmented Lagrangian method (ALM) are used to solve the proposed model.
With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference.
Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment.
American Psychological Association (APA)
He, Chuan& Hu, Changhua& Yang, Xiaogang& He, Hua-Feng& Zhang, Qi. 2014. An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-450418
Modern Language Association (MLA)
He, Chuan…[et al.]. An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising. Mathematical Problems in Engineering No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-450418
American Medical Association (AMA)
He, Chuan& Hu, Changhua& Yang, Xiaogang& He, Hua-Feng& Zhang, Qi. An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-450418
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-450418