Blind Image Super Resolution

Blind image super-resolution (SR) aims to reconstruct high-resolution images from low-resolution counterparts with unknown degradation types, a crucial challenge in real-world image processing. Current research heavily focuses on implicit degradation modeling, often employing transformer-based architectures or diffusion models, and exploring techniques like contrastive learning and uncertainty suppression to improve the robustness and accuracy of SR. These advancements are significant because they enable higher-quality image upscaling in diverse applications, from medical imaging to remote sensing, where precise degradation knowledge is often unavailable. The development of new datasets specifically designed for real-world blur and RAW images further enhances the field's ability to create more realistic and effective SR models.