Image super-resolution is a fundamental task in computer vision that focuses on enhancing a low-resolution image into the corresponding high-resolution image. It is a difficult task because of the ill-posed and challenging content of the images. High-resolution images often cannot be reconstructed from low-resolution images, and low-resolution images can potentially yield multiple plausible high-resolution images. Recently, diffusion models have emerged as a promising approach to super-resolution, offering valuable insights into traditional deep-learning methods. In this paper, we comprehensively review existing diffusion model literature and their applications for super-resolution, discussing their performance on various benchmarks. Subsequently, the paper surveys an objective analysis of diffusion-based models and highlights the key advantages and challenges. This review aims to provide a unified understanding of the principles and current trends in image super-resolution using diffusion models, inspiring further progress and advancements in this rapidly evolving area.
Aala et al. (Mon,) studied this question.