Learning Epipolar-Spatial Relationship for Light Field Image Super-Resolution

Ahmed Salem, Hatem Ibrahem, Hyun-Soo Kang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 1336-1345

Light field (LF) imaging has become increasingly popular in recent years for capturing and processing visual information. A significant challenge in LF processing is super-resolution (SR), which aims to enhance the resolution of low-resolution LF images. This article proposes a new LF image super-resolution (LFSR) approach that leverages the epipolar-spatial relationship within the LF. To train a deep neural network for LFSR, the proposed method involves extracting three types of information from the LF: spatial, horizontal epipolar, and vertical epipolar. Experimental results demonstrate the effectiveness of the proposed approach compared with state-of-the-art (SOTA) performance, as evidenced by quantitative metrics and visual quality. In addition, we conducted ablation studies to assess the effectiveness of each type of information and gain insights into the underlying mechanisms of the proposed method. Our approach achieved competitive results on the NTIRE 2023 Light Field Image Super-Resolution Challenge: our proposed model was ranked 10th on the test set and 6th on the validation set among 148 participants. Paper's code is available at: https://github.com/ahmeddiefy/EpiS_LFSR.