Diffusion Models, Image Super-Resolution, and Everything: A Survey.

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.

Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances.

With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that require further research, e.g.