  Exploiting spatial-angular correlation is crucial to light field (LF) image
super-resolution (SR), but is highly challenging due to its non-local property
caused by the disparities among LF images. Although many deep neural networks
(DNNs) have been developed for LF image SR and achieved continuously improved
performance, existing methods cannot well leverage the long-range
spatial-angular correlation and thus suffer a significant performance drop when
handling scenes with large disparity variations. In this paper, we propose a
simple yet effective method to learn the non-local spatial-angular correlation
for LF image SR. In our method, we adopt the epipolar plane image (EPI)
representation to project the 4D spatial-angular correlation onto multiple 2D
EPI planes, and then develop a Transformer network with repetitive
self-attention operations to learn the spatial-angular correlation by modeling
the dependencies between each pair of EPI pixels. Our method can fully
incorporate the information from all angular views while achieving a global
receptive field along the epipolar line. We conduct extensive experiments with
insightful visualizations to validate the effectiveness of our method.
Comparative results on five public datasets show that our method not only
achieves state-of-the-art SR performance, but also performs robust to disparity
variations. Code is publicly available at
https://github.com/ZhengyuLiang24/EPIT.

Computer Vision and Pattern Recognition