DCM-CNN: Densely Connected Multiloss Convolutional Neural Networks for Light Field View Synthesis

Different from traditional cameras, the light field cameras record spatial information with its angular information. Since the angular and spatial resolutions are limited due to the hardware shortage, view synthesis is required to provide arbitrary views. In this paper, we propose densely connected multiloss convolutional neural networks for light field view synthesis, called DCM-CNN. We build DCM-CNN for view synthesis based on the feature reuse, thereby leading to low computational complexity. Moreover, we present a multiloss function that consists of pixel loss, feature loss and edge loss to produce accurate edges and textures in the synthesized views. Experimental results show that the proposed method generates high quality views from light field data with low computational complexity and outperforms state-of-the-art ones in terms of structural similarity (SSIM), peak-signal-to-noise ratio (PSNR), and runtime.