  We introduce Frequency Domain Perceptual Loss (FDPL), a loss function for
single image super resolution (SR). Unlike previous loss functions used to
train SR models, which are all calculated in the pixel (spatial) domain, FDPL
is computed in the frequency domain. By working in the frequency domain we can
encourage a given model to learn a mapping that prioritizes those frequencies
most related to human perception. While the goal of FDPL is not to maximize the
Peak Signal to Noise Ratio (PSNR), we found that there is a correlation between
decreasing FDPL and increasing PSNR. Training a model with FDPL results in a
higher average PSRN (30.94), compared to the same model trained with pixel loss
(30.59), as measured on the Set5 image dataset. We also show that our method
achieves higher qualitative results, which is the goal of a perceptual loss
function. However, it is not clear that the improved perceptual quality is due
to the slightly higher PSNR or the perceptual nature of FDPL.
