Name
Abstract | ||
|---|---|---|
•The kernel Wiener filtering model is proposed as an extension of the conventional Wiener filtering by introducing a shape-aware kernel function.•The constrained covariance matrix nuclear norm is minimized to estimate the reference images.•Eigenvalue thresholding is deduced for the shrinkage of transformed coefficients in the optimized low-rank approximation.•The experiments verify the generality and effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2018.06.028 | Information Sciences |
Keywords | Field | DocType |
Image denoising,Sparse representation,Low-rank approximation,Wiener filtering,Color space | Wiener filter,Kernel (linear algebra),Sparse approximation,Algorithm,Low-rank approximation,Image denoising,Artificial intelligence,Thresholding,Machine learning,Mathematics,Eigenvalues and eigenvectors,Kernel (statistics) | Journal |
Volume | ISSN | Citations |
462 | 0020-0255 | 2 |
PageRank | References | Authors |
0.44 | 34 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongqin Zhang | 1 | 83 | 6.54 |
| jinsheng xiao | 2 | 54 | 7.78 |
| Jinye Peng | 3 | 284 | 40.93 |
| yu ding | 4 | 45 | 2.85 |
| Jiaying Liu | 5 | 860 | 83.96 |
| Zongming Guo | 6 | 778 | 81.98 |
| Xiaopeng Zong | 7 | 9 | 1.42 |