Name
Abstract | ||
|---|---|---|
Low-rank approximation-based methods have recently achieved impressive results in image restoration. Generally, the low-rank constraint integrated with the nonlocal self-similarity prior is enforced for image recovery. However, it is still unsatisfactory to recover complex image structures due to the lack of joint modeling based on local and global information, especially when the signal-to-noise ratio is low. In this paper, we propose a novel structure-constrained low-rank approximation method using complementary local and global information, as, respectively, modeled by kernel Wiener filtering and low-rank regularization. The proposed method solves the ill-posed inverse problem associated with image denoising by the alternating direction method of multipliers. Experimental results demonstrate that the proposed method not only removes noise effectively, but also is highly competitive against the state-of-the-art methods both qualitatively and quantitatively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00521-020-04717-w | NEURAL COMPUTING & APPLICATIONS |
Keywords | DocType | Volume |
Image denoising,Sparse representation,Low-rank approximation,Wiener filtering,Deep learning | Journal | 32.0 |
Issue | ISSN | Citations |
16.0 | 0941-0643 | 1 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongqin Zhang | 1 | 83 | 6.54 |
| Ruiwen Kang | 2 | 1 | 0.36 |
| Xianlin Peng | 3 | 2 | 1.73 |
| Jun Wang | 4 | 3 | 3.42 |
| Jihua Zhu | 5 | 59 | 18.64 |
| Jinye Peng | 6 | 284 | 40.93 |
| Hangfan Liu | 7 | 83 | 8.31 |