Light field (LF) cameras record both intensity and directions of light rays,
and capture scenes from a number of viewpoints. Both information within each
perspective (i.e., spatial information) and among different perspectives (i.e.,
angular information) is beneficial to image super-resolution (SR). In this
paper, we propose a spatial-angular interactive network (namely, LF-InterNet)
for LF image SR. Specifically, spatial and angular features are first
separately extracted from input LFs, and then repetitively interacted to
progressively incorporate spatial and angular information. Finally, the
interacted features are fused to superresolve each sub-aperture image.
Experimental results demonstrate the superiority of LF-InterNet over the
state-of-the-art methods, i.e., our method can achieve high PSNR and SSIM
scores with low computational cost, and recover faithful details in the
reconstructed images.

本文提出一种名为 LF-InterNet 的空间角度交互网络用于光场图像超分辨率，通过特征交互逐渐融合空间和角度信息。我们的方法在低计算成本下，能够以高 PSNR 和 SSIM 评分还原重构图像的细节，明显优于现有的超分辨率方法。