  Light field (LF) cameras can record scenes from multiple perspectives, and
thus introduce beneficial angular information for image super-resolution (SR).
However, it is challenging to incorporate angular information due to
disparities among LF images. In this paper, we propose a deformable convolution
network (i.e., LF-DFnet) to handle the disparity problem for LF image SR.
Specifically, we design an angular deformable alignment module (ADAM) for
feature-level alignment. Based on ADAM, we further propose a
collect-and-distribute approach to perform bidirectional alignment between the
center-view feature and each side-view feature. Using our approach, angular
information can be well incorporated and encoded into features of each view,
which benefits the SR reconstruction of all LF images. Moreover, we develop a
baseline-adjustable LF dataset to evaluate SR performance under different
disparity variations. Experiments on both public and our self-developed
datasets have demonstrated the superiority of our method. Our LF-DFnet can
generate high-resolution images with more faithful details and achieve
state-of-the-art reconstruction accuracy. Besides, our LF-DFnet is more robust
to disparity variations, which has not been well addressed in literature.
