| Paper ID | IVMSP-6.3 |
| Paper Title |
HYPERSPECTRAL IMAGE SUPER-RESOLUTION VIA ADJACENT SPECTRAL FUSION STRATEGY |
| Authors |
Qiang Li, Qi Wang, Xuelong Li, Northwestern Polytechnical University, China |
| Session | IVMSP-6: Super-resolution 2 & Multi-scale Processing |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Hyperspectral image exhibits low spatial resolution due to the limitation of imaging system. Improving it without an auxiliary high resolution (HR) image still remains a challenging problem. Recently, although many deep learning-based hyperspectral image super-resolution (SR) methods have been proposed, they make the insufficient utilization of adjacent bands to improve the reconstruction performance. To address this issue, we explore a new structure for hyperspectral image SR via adjacent spectral fusion strategy. Inspired by the high similarity among adjacent bands, neighboring band partition is proposed to divide the adjacent bands into several groups. Through the current band, the adjacent bands is guided to enhance the exploration ability. To explore more complementary information, an alternative fusion mechanism, i.e., intra-group fusion and inter-group fusion, is designed, which helps to recover the missing details in the current band. Experiments demonstrate that our approach produces the state-of-the-art results over the existing approaches. |
