  Recently, there has been discussions on the ill-posed nature of
super-resolution that multiple possible reconstructions exist for a given
low-resolution image. Using normalizing flows, SRflow[23] achieves
state-of-the-art perceptual quality by learning the distribution of the output
instead of a deterministic output to one estimate. In this paper, we adapt the
concepts of SRFlow to improve GAN-based super-resolution by properly
implementing the one-to-many property. We modify the generator to estimate a
distribution as a mapping from random noise. We improve the content loss that
hampers the perceptual training objectives. We also propose additional training
techniques to further enhance the perceptual quality of generated images. Using
our proposed methods, we were able to improve the performance of ESRGAN[1] in
x4 perceptual SR and achieve the state-of-the-art LPIPS score in x16 perceptual
extreme SR by applying our methods to RFB-ESRGAN[21].
