Diffusion Models (DMs) represent a significant advancement in image
Super-Resolution (SR), aligning technical image quality more closely with human
preferences and expanding SR applications. DMs address critical limitations of
previous methods, enhancing overall realism and details in SR images. However,
DMs suffer from color-shifting issues, and their high computational costs call
for efficient sampling alternatives, underscoring the challenge of balancing
computational efficiency and image quality. This survey gives an overview of
DMs applied to image SR and offers a detailed analysis that underscores the
unique characteristics and methodologies within this domain, distinct from
broader existing reviews in the field. It presents a unified view of DM
fundamentals and explores research directions, including alternative input
domains, conditioning strategies, guidance, corruption spaces, and zero-shot
methods. This survey provides insights into the evolution of image SR with DMs,
addressing current trends, challenges, and future directions in this rapidly
evolving field.

本综述论文对应用于图像超分辨率的扩散模型（DMs）进行了概述，并进行了详细分析，强调了该领域的独特特征和方法论。它展示了 DM 基础知识的统一视角，并探索了包括替代输入域、条件策略、指导、失真空间和零样本方法等研究方向。此综述论文洞察了 DMs 在图像超分辨率中的演变，讨论了当前的趋势、挑战和未来方向。