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.

翻译：扩散模型在图像超分辨率领域取得了重大突破，使技术图像质量更贴近人类偏好，并扩展了超分辨率应用场景。扩散模型有效克服了先前方法的局限性，显著提升了超分辨率图像的逼真度与细节表现。然而，扩散模型存在色彩偏移缺陷，且其高计算成本催生了高效采样替代方案的需求，这使得平衡计算效率与图像质量成为关键挑战。本综述全面梳理了应用于图像超分辨率的扩散模型，通过详细分析揭示了该领域区别于现有泛化研究的独特特征与方法论。我们统一阐述了扩散模型基本原理，探讨了包括替代输入域、条件策略、引导机制、噪声空间及零样本方法在内的研究方向。本综述为扩散模型驱动的图像超分辨率演进提供了深刻洞见，梳理了该快速演进领域的技术趋势、现存挑战与未来方向。