Diffusion Models (DMs) have disrupted the image Super-Resolution (SR) field and further closed the gap between image quality and human perceptual preferences. They are easy to train and can produce very high-quality samples that exceed the realism of those produced by previous generative methods. Despite their promising results, they also come with new challenges that need further research: high computational demands, comparability, lack of explainability, color shifts, and more. Unfortunately, entry into this field is overwhelming because of the abundance of publications. To address this, we provide a unified recount of the theoretical foundations underlying DMs applied to image SR and offer a detailed analysis that underscores the unique characteristics and methodologies within this domain, distinct from broader existing reviews in the field. This survey articulates a cohesive understanding of DM principles and explores current research avenues, including alternative input domains, conditioning techniques, guidance mechanisms, corruption spaces, and zero-shot learning approaches. By offering a detailed examination of the evolution and current trends in image SR through the lens of DMs, this survey sheds light on the existing challenges and charts potential future directions, aiming to inspire further innovation in this rapidly advancing area.

翻译：扩散模型（DMs）已颠覆图像超分辨率（SR）领域，并进一步缩小了图像质量与人类感知偏好之间的差距。它们易于训练，能够生成质量极高的样本，其真实感超越了先前生成方法所产生的结果。尽管成果显著，它们也带来了新的挑战，亟需进一步研究：高计算需求、可比性不足、可解释性缺乏、色彩偏移等。遗憾的是，由于相关文献数量庞大，进入该领域令人望而生畏。为此，我们对应用于图像SR的DMs的理论基础进行了统一梳理，并提供了详细分析，以强调该领域内区别于现有更广泛综述的独特特性与方法。本综述系统阐述了DM原理的连贯理解，并探索了当前的研究路径，包括替代输入域、条件技术、引导机制、退化空间以及零样本学习方法。通过从DMs的视角详细审视图像SR的演进与当前趋势，本综述揭示了现有挑战并规划了潜在的未来方向，旨在激发这一快速发展领域的进一步创新。