  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.
