  Large scale image super-resolution is a challenging computer vision task,
since vast information is missing in a highly degraded image, say for example
forscale x16 super-resolution. Diffusion models are used successfully in recent
years in extreme super-resolution applications, in which Gaussian noise is used
as a means to form a latent photo-realistic space, and acts as a link between
the space of latent vectors and the latent photo-realistic space. There are
quite a few sophisticated mathematical derivations on mapping the statistics of
Gaussian noises making Diffusion Models successful. In this paper we propose a
simple approach which gets away from using Gaussian noise but adopts some basic
structures of diffusion models for efficient image super-resolution.
Essentially, we propose a DNN to perform domain transfer between neighbor
domains, which can learn the differences in statistical properties to
facilitate gradual interpolation with results of reasonable quality. Further
quality improvement is achieved by conditioning the domain transfer with
reference to the input LR image. Experimental results show that our method
outperforms not only state-of-the-art large scale super resolution models, but
also the current diffusion models for image super-resolution. The approach can
readily be extended to other image-to-image tasks, such as image enlightening,
inpainting, denoising, etc.
