Name
Abstract | ||
|---|---|---|
The traditional robust principal component analysis (RPCA) via decomposition into low-rank plus sparse matrices offers a powerful framework for a large variety of applications in computer vision. However, the reconstructed image experiences serious interference by Gaussian noise, resulting in the degradation of image quality during the denoising process. Thus, a novel manifold constrained joint sparse learning (MCJSL) via non-convex regularization approach is proposed in this paper. Morelly, the manifold constraint is adopted to preserve the local geometric structures and the non-convex joint sparsity is introduced to capture the global row-wise sparse structures. To solve MCJSL, an efficient optimization algorithm using the manifold alternating direction method of multipliers (MADMM) is designed with closed-form solutions and it achieves a fast and convergent procedure. Moreover, the convergence is analyzed mathematically and numerically. Comparisons among the proposed MCJSL and some state-of-the-art solvers, on several accessible datasets, are presented to demonstrate its superiority in image denoising and background subtraction. The results indicate the importance to incorporate the manifold learning and non-convex joint sparse regularization into a general RPCA framework. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.06.008 | Neurocomputing |
Keywords | DocType | Volume |
Robust principal component analysis (RPCA),Manifold constrained joint sparse learning (MCJSL),Non-convex regularization,Manifold alternating direction method of multipliers | Journal | 458 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingjing Liu | 1 | 0 | 1.69 |
| Xianchao Xiu | 2 | 3 | 3.45 |
| X. Jiang | 3 | 19 | 6.91 |
| Wanquan Liu | 4 | 629 | 81.29 |
| Xiaoyang Zeng | 5 | 442 | 107.26 |
| Mingyu Wang | 6 | 0 | 1.69 |
| Hui Chen | 7 | 3 | 1.46 |