Name
Abstract | ||
|---|---|---|
Mixture noise removal is a fundamental problem in hyperspectral images' (HSIs) processing that holds significant practical importance for subsequent applications. This problem can be recast as an approximation issue of a low-rank matrix. In this paper, a novel smooth rank approximation (SRA) model is proposed to cope with these mixture noises for HSIs. The crux idea is to devise a general smooth f... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TGRS.2019.2891288 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Noise reduction,Gaussian noise,Matrix decomposition,Hyperspectral imaging,Mathematical model,Iterative methods,Data models | Convergence (routing),Noise reduction,Data modeling,Computer vision,Iterative method,Matrix decomposition,Algorithm,Hyperspectral imaging,Artificial intelligence,Gaussian noise,Mathematics,Convex analysis | Journal |
Volume | Issue | ISSN |
57 | 7 | 0196-2892 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hailiang Ye | 1 | 32 | 2.94 |
| Hong Li | 2 | 209 | 19.76 |
| Bing Yang | 3 | 44 | 8.37 |
| Feilong Cao | 4 | 31 | 2.59 |
| Yuan Yan Tang | 5 | 2662 | 209.20 |