Blind Super Resolution

Blind super-resolution (BSR) aims to reconstruct high-resolution images from low-resolution counterparts without knowing the degradation process, a crucial challenge in image processing. Current research heavily focuses on implicit degradation modeling, employing techniques like contrastive learning, diffusion models, and transformers to learn complex degradation patterns directly from data, often within iterative or alternating optimization frameworks. These advancements improve the accuracy and efficiency of BSR, particularly for real-world images with unknown and varied degradations. The resulting improvements have significant implications for various applications, including satellite imagery analysis, medical imaging, and enhancing the quality of consumer photos and videos.