Training Generative Image Super-Resolution Models by Wavelet-Domain
  Losses Enables Better Control of Artifacts
Cansu Korkmaz A. Murat Tekalp Zafer Dogan
Super-resolution (SR) is an ill-posed inverse problem, where the size of the
set of feasible solutions that are consistent with a given low-resolution image
is very large. Many algorithms have been proposed to find a "good" solution
among the feasible solutions that strike a balance between fidelity and
perceptual quality. Unfortunately, all known methods generate artifacts and
hallucinations while trying to reconstruct high-frequency (HF) image details. A
fundamental question is: Can a model learn to distinguish genuine image details
from artifacts? Although some recent works focused on the differentiation of
details and artifacts, this is a very challenging problem and a satisfactory
solution is yet to be found. This paper shows that the characterization of
genuine HF details versus artifacts can be better learned by training GAN-based
SR models using wavelet-domain loss functions compared to RGB-domain or
Fourier-space losses. Although wavelet-domain losses have been used in the
literature before, they have not been used in the context of the SR task. More
specifically, we train the discriminator only on the HF wavelet sub-bands
instead of on RGB images and the generator is trained by a fidelity loss over
wavelet subbands to make it sensitive to the scale and orientation of
structures. Extensive experimental results demonstrate that our model achieves
better perception-distortion trade-off according to multiple objective measures
and visual evaluations.