Recent Developments in Diffusion-Based Image Super-Resolution: A Comprehensive Survey

Abstract

Image super-resolution (SR) is a fundamental task in computer vision that has received significant attention due to its applications in enhancing image quality across various fields, from medical imaging to satellite data processing. The emergence of diffusion models and advanced deep learning techniques has transformed how super-resolution is approached, offering novel frameworks to improve low-resolution images with unprecedented accuracy. This paper presents a detailed survey of the latest advancements in diffusion-based models for SR, exploring methodologies such as wavelet amplification, federated learning, and dataset pruning. We discuss not only the theoretical underpinnings of these approaches but also their real-world implications, particularly in blind SR tasks where ground truth high-resolution data is unavailable. Furthermore, we provide an overview of current challenges, such as computational complexity and the need for better generalization in unseen domains, along with potential solutions. The analysis covers six key contributions to the field from recent research papers, all of which have significantly advanced our understanding and capabilities in image super-resolution. By synthesizing these developments, this survey aims to serve as a comprehensive resource for researchers and practitioners in the field.