Light Field Synthesis from a Single Image using Improved Wasserstein Generative Adversarial Network

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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present a deep learning-based method to synthesize a 4D light field from a single 2D RGB image. We consider the light field synthesis problem equivalent to image super-resolution, and solve it by using the improved Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP). Experimental results demonstrate that our algorithm can predict complex occlusions and relative depths in challenging scenes. The light fields synthesized by our method has much higher signal-to-noise ratio and structural similarity than the state-of-the-art approach.
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@inproceedings{
10.2312:egp.20181017
, booktitle = {
EG 2018 - Posters
}, editor = {
Jain, Eakta and Kosinka, Jirí
}, title = {{
Light Field Synthesis from a Single Image using Improved Wasserstein Generative Adversarial Network
}}, author = {
Ruan, Lingyan
and
Chen, Bin
and
Lam, Miu Ling
}, year = {
2018
}, publisher = {
The Eurographics Association
}, ISSN = {
1017-4656
}, ISBN = {}, DOI = {
10.2312/egp.20181017
} }
Citation