Light-field Image Super-resolution Using Convolutional Neural Network

Abstract

Light-field cameras can capture 2D spatial and 2D angular information in a single shot. Nevertheless, light-field cameras usually have a trade-off between the spatial and angular resolution in a restricted sensor resolution. The low spatial resolution of light-field cameras limits the application of light-field cameras. In this paper, we present a novel light-field super-resolution method based on convolutional neural network (CNN). With low-resolution light-field multiview images as input, we directly learn the mapping between low-resolution images and high-resolution images by developing an end-to-end CNN. Experimental results demonstrate that in terms of visual effects and evaluation metrics, the reconstruction results of the proposed methods is superior to those of state-of-the-art methods. The proposed approach takes advantage of useful information among the multiple views of light fields for super-resolution reconstruction.