Ruan, LingyanChen, BinLam, Miu LingJain, Eakta and Kosinka, Jirí2018-04-142018-04-1420181017-4656https://doi.org/10.2312/egp.20181017https://diglib.eg.org:443/handle/10.2312/egp20181017We 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.Computing methodologiesMachine learningComputational photography10.2312/egp.2018101719-20