Blind SR

Blind super-resolution (SR) aims to reconstruct high-resolution images from low-resolution counterparts without prior knowledge of the degradation process, a significant challenge in image processing. Current research focuses on improving degradation estimation accuracy, often employing deep learning models like diffusion probabilistic models and integrating these estimations into high-performance SR networks through novel architectures. This field is crucial for various applications, including remote sensing and medical imaging, where image quality enhancement is critical and degradation characteristics are often unknown or complex. The development of robust and accurate blind SR methods is driving advancements in both image processing algorithms and federated learning techniques for privacy-preserving data utilization.