| Paper ID | IVMSP-30.5 |
| Paper Title |
MULTI-MODELS FUSION FOR LIGHT FIELD ANGULAR SUPER-RESOLUTION |
| Authors |
FengYin Cao, Ping An, Xinpeng Huang, Chao Yang, Shanghai University, China; Qiang Wu, University of Technology Sydney, Australia |
| Session | IVMSP-30: Inverse Problems in Image & Video Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVELI] Electronic Imaging |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Light field (LF) imaging has received increasing attention due to its richer interpretation of the scene. However, an inherent spatial-angular trade-off exists in LF that prevents LF from practical applications. Consequently, how to break such a trade-off has become one of the main challenges in sparsely sampled LF reconstruction. LF super-resolution (SR) can provide an opportunity to solve this issue, but most methods exploit only one form of LF, thereby leading to much loss of information. We believe that different LF forms can compensate each other to obtain higher gains via fusion strategy. In this paper, therefore, we propose a multi-models fusion for LF SR in angular domain. Cascading models which are trained by different LF forms can fully exploit rich LF information. Experimental results demonstrate that our method is effective and achieves a comparable result against state-of-the-art techniques. |
