Super-Resolution for Near-Eye Light Field Display in Fourier Space

Near-eye light field displays are superior to conventional AR displays because they offer continuous focus and viewing experiences free from visual accommodation conflict (VAC). However, given a fixed number of pixels for representation of the spatio-angular information of a light field, the inherent tradeoff between angular and spatial resolutions presents a great challenge to the widespread adoption of light field technology. To address the challenge, we propose a hybrid super-resolution framework consisting of a digital neural network and an optical neural network and allowing an end-to-end optimization of the downsampling operation for fitting the light field data into a fixed-resolution display panel and the upsampling operation for enhancing the light field quality, all in the frequency domain. Experimental results show that the proposed hybrid framework is a promising approach to quality enhancement of near-eye light field displays.