Name
Title | ||
|---|---|---|
Weighted Schatten $p$-Norm Minimization for Image Denoising and Background Subtraction |
Abstract | ||
|---|---|---|
Low rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort to using the nuclear norm minimization (NNM) as a convex relaxation of the nonconvex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank compon... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIP.2016.2599290 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Wireless sensor networks,Minimization,Optimization,Electronic mail,Sparse matrices,Image denoising,Computational modeling | Background subtraction,Applied mathematics,Singular value,Minification,Artificial intelligence,Sparse matrix,Discrete mathematics,Pattern recognition,Permutation,Low-rank approximation,Norm (mathematics),Iterated function,Mathematics | Journal |
Volume | Issue | ISSN |
25 | 10 | 1057-7149 |
Citations | PageRank | References |
49 | 0.94 | 40 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Xie | 1 | 407 | 27.48 |
| Shuhang Gu | 2 | 701 | 28.25 |
| Liu Yan | 3 | 828 | 41.20 |
| Wangmeng Zuo | 4 | 3833 | 173.11 |
| Wensheng Zhang | 5 | 323 | 28.76 |
| Lei Zhang | 6 | 16326 | 543.99 |