| Paper ID | D-3-3.1 |
| Paper Title |
DEEP LEARNING BASED DEPTH ESTIMATION AND RECONSTRUCTION OF LIGHT FIELD IMAGES |
| Authors |
Jae-Seong Yun, Jae-Young Sim, UNIST, Korea (South) |
| Session |
D-3-3: Image and video processing based on deep learning |
| Time | Thursday, 10 December, 17:30 - 19:30 |
| Presentation Time: | Thursday, 10 December, 17:30 - 17:45 Check your Time Zone |
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All times are in New Zealand Time (UTC +13) |
| Topic |
Image, Video, and Multimedia (IVM): Special Session: Image and video processing based on deep learning |
| Abstract |
Light field imaging is one of the most promising methods to capture realistic 3D scenes. In this paper, we propose a deep learning network composed of two sub-networks performing depth estimation and light field image reconstruction, respectively. We simultaneously train the two sub-networks by employing a loss function combining the reconstruction loss of the reconstruction network and the estimation loss of the depth estimation network. Experimental results demonstrate that the proposed method accurately estimates the disparity maps of light field images and also faithfully reconstructs light field images. |