A Dual Alternating Direction Method of Multipliers for Image Decomposition and Restoration
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2019
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910753076805632 |
|---|---|
| author | Wang, Qingsong Wang, Chengjing Tang, Peipei Niu, Dunbiao |
| author_facet | Wang, Qingsong Wang, Chengjing Tang, Peipei Niu, Dunbiao |
| contents | In this paper, we develop a dual alternating direction method of multipliers (ADMM) for an image decomposition model. In this model, an image is divided into two meaningful components, i.e., a cartoon part and a texture part. The optimization algorithm that we develop not only gives the cartoon part and the texture part of an image but also gives the restored image (cartoon part + texture part). We also present the global convergence and the local linear convergence rate for the algorithm under some mild conditions. Numerical experiments demonstrate the efficiency and robustness of the dual ADMM (dADMM). Furthermore, we can obtain relatively higher signalto-noise ratio (SNR) comparing to other algorithms. It shows that the choice of the algorithm is also important even for the same model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_05361 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | A Dual Alternating Direction Method of Multipliers for Image Decomposition and Restoration Wang, Qingsong Wang, Chengjing Tang, Peipei Niu, Dunbiao Image and Video Processing Numerical Analysis In this paper, we develop a dual alternating direction method of multipliers (ADMM) for an image decomposition model. In this model, an image is divided into two meaningful components, i.e., a cartoon part and a texture part. The optimization algorithm that we develop not only gives the cartoon part and the texture part of an image but also gives the restored image (cartoon part + texture part). We also present the global convergence and the local linear convergence rate for the algorithm under some mild conditions. Numerical experiments demonstrate the efficiency and robustness of the dual ADMM (dADMM). Furthermore, we can obtain relatively higher signalto-noise ratio (SNR) comparing to other algorithms. It shows that the choice of the algorithm is also important even for the same model. |
| title | A Dual Alternating Direction Method of Multipliers for Image Decomposition and Restoration |
| topic | Image and Video Processing Numerical Analysis |
| url | https://arxiv.org/abs/1901.05361 |