Name
Abstract | ||
|---|---|---|
Hyperspectral image (HSI) denoising is challenging not only because of the difficulty in preserving both spectral and spatial structures simultaneously, but also due to the requirement of removing various noises, which are often mixed together. In this paper, we present a nonconvex low rank matrix approximation (NonLRMA) model and the corresponding HSI denoising method by reformulating the approxi... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TGRS.2017.2706326 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Noise reduction,Sparse matrices,Gaussian noise,Tensile stress,Robustness,Hyperspectral imaging | Matrix (mathematics),Augmented Lagrangian method,Artificial intelligence,Sparse matrix,Computer vision,Mathematical optimization,Iterative method,Algorithm,Hyperspectral imaging,Matrix norm,Low-rank approximation,Gaussian noise,Mathematics | Journal |
Volume | Issue | ISSN |
55 | 9 | 0196-2892 |
Citations | PageRank | References |
12 | 0.51 | 31 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongyong Chen | 1 | 74 | 12.11 |
| Yan-Wen Guo | 2 | 348 | 39.32 |
| Yong-li Wang | 3 | 107 | 26.46 |
| Dong Wang | 4 | 104 | 22.28 |
| Chong Peng | 5 | 288 | 20.54 |
| Guoping He | 6 | 91 | 13.59 |