Name
Abstract | ||
|---|---|---|
Restoration of hyperspectral images (HSIs) is a challenging task, owing to the reason that images are inevitably contaminated by a mixture of noise, including Gaussian noise, impulse noise, dead lines, and stripes, during their acquisition process. Recently, HSI denoising approaches based on low-rank matrix approximation have become an active research field in remote sensing and have achieved stat... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LGRS.2017.2700406 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Matrix decomposition,Noise reduction,Sparse matrices,Hyperspectral imaging,Image restoration,Gaussian noise | Singular value decomposition,Computer vision,Pattern recognition,Matrix (mathematics),Matrix decomposition,Robust principal component analysis,Low-rank approximation,Artificial intelligence,Impulse noise,Gaussian noise,Sparse matrix,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 7 | 1545-598X |
Citations | PageRank | References |
2 | 0.37 | 16 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Xu | 1 | 5 | 1.10 |
| Yongyong Chen | 2 | 74 | 12.11 |
| Chong Peng | 3 | 288 | 20.54 |
| Yong-li Wang | 4 | 107 | 26.46 |
| Xuefeng Liu | 5 | 2 | 0.37 |
| Guoping He | 6 | 91 | 13.59 |