Learning from EPI-Volume-Stack for Light Field image angular super-resolution

Light Field (LF) image angular super-resolution aims to synthesize a high angular resolution LF image from a low angular resolution one, and is drawing increased attention because of its wide applications. In order to reconstruct a high angular resolution LF image, many learning based LF image angular super-resolution methods have been proposed. However, most existing methods are based on LF Epipolar Plane Image or Epipolar Plane Image volume representation, which underuse the LF image structure. The LF view spatial correlation and neighboring LF views angular correlations which can reflect LF image structure are not fully explored, which reduces LF angular super-resolution quality. In order to alleviate this problem, this paper introduces an Epipolar Plane Image Volume Stack (EPI-VS) representation for LF angular super-resolution. The EPI-VS is constituted by arranging all LF views in a raster order, which benefits in exploring LF view spatial correlation and neighboring LF views angular correlations. Based on such representation, we further propose an LF angular super-resolution network. 3D convolutions are applied in the whole super-resolution network to better accommodate the input EPI-VS data and allow information propagation between two spatial and one directional dimensions of EPI-VS data. Extensive experiments on synthetic and real-world LF scenes demonstrate the effectiveness of the proposed network. Moreover, we also illustrate the superiority of our network by applying it in scene depth estimation task