Name
Abstract | ||
|---|---|---|
Hyperspectral image fusion (HIF) reconstructs high spatial resolution hyperspectral images from low spatial resolution hyperspectral images and high spatial resolution multispectral images. Previous works usually assume that the linear mapping between the point spread functions of the hyperspectral camera and the spectral response functions of the conventional camera is known. This is unrealistic in many scenarios. We propose a method for blind HIF problem based on deep learning, where the estimation of the observation model and fusion process are optimized iteratively and alternatingly during the super-resolution reconstruction. In addition, the proposed framework enforces simultaneous spatial and spectral accuracy. Using three public datasets, the experimental results demonstrate that the proposed algorithm outperforms existing blind and nonblind methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCV.2019.00425 | 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) |
Field | DocType | Volume |
Computer vision,Pattern recognition,Image fusion,Computer science,Hyperspectral imaging,Artificial intelligence | Conference | 2019 |
Issue | ISSN | Citations |
1 | 1550-5499 | 1 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wu Wang | 1 | 3 | 1.03 |
| Weihong Zeng | 2 | 1 | 1.02 |
| Yue Huang | 3 | 35 | 6.24 |
| Xinghao Ding | 4 | 591 | 52.95 |
| John Paisley | 5 | 54 | 4.63 |