Image Restoration Problem

Image restoration aims to recover high-quality images from degraded versions, addressing challenges like noise, blur, and missing data. Current research heavily utilizes deep learning, focusing on diffusion models and variational autoencoders as generative priors, often incorporated into iterative algorithms with techniques like preconditioned guidance to improve efficiency and robustness. These advancements are improving the speed and accuracy of image restoration across various applications, including medical imaging, satellite imagery, and video processing, while also emphasizing uncertainty quantification and the generation of meaningfully diverse restoration outputs.