High-Quality Virtual View Synthesis for Light Field Cameras Using Multi-Loss Convolutional Neural Networks
Zicheng Nian, Cheolkon Jung · 2018
Although light field cameras record both spatial and angular information, their angular and spatial resolutions are limited when capturing light field data. Thus, it is required to synthesize virtual views. In this paper, we propose high-quality virtual view synthesis based on multi -loss convolutional neural networks (CNN). We adopt multi-loss function for view synthesis in both pixel and feature spaces to increase the angular resolution of light field data. We combine three losses of feature loss, edge loss, and mean squared error (MSE) loss into the multi -loss function. We learn the view synthesis function based on simple three layers of CNN. Experimental results show that the proposed method successfully produces virtual views from light field data and outperforms state-of-the-arts in terms of peak-signal-to-ratio (PSNR) and structural similarity (SSIM).