  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.
