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.

翻译：大规模图像超分辨率是一项具有挑战性的计算机视觉任务，因为严重退化图像（例如16倍超分辨率）中大量信息缺失。近年来，扩散模型在极端超分辨率应用中取得显著成功，其中高斯噪声被用于构建潜在逼真空间，并作为潜在向量空间与该空间之间的桥梁。已有许多精密的数学推导用于映射高斯噪声的统计特性，从而使扩散模型取得成功。本文提出一种简洁方法，该方法摒弃了高斯噪声的使用，而是借鉴扩散模型的基本结构以实现高效的图像超分辨率。具体而言，我们提出一种深度神经网络（DNN）在相邻域之间执行域迁移，通过学习其统计特性差异，实现渐进插值并获得质量合理的结果。通过参考输入低分辨率图像对域迁移进行条件约束，可进一步提升质量。实验结果表明，我们的方法不仅优于当前最先进的大规模超分辨率模型，也优于现有的图像超分辨率扩散模型。该方案可轻松扩展至其他图像到图像任务，如图像增强、修复、去噪等。