Image Restoration Framework

Image restoration frameworks aim to recover high-quality images from degraded versions, addressing issues like blur, noise, and missing data. Current research emphasizes developing efficient and accurate algorithms, often integrating deep learning models (like U-Nets and transformers) with Bayesian approaches for improved uncertainty quantification and handling of diverse degradation types. These advancements leverage techniques such as contrastive learning, variational inference, and the incorporation of sophisticated image priors to achieve state-of-the-art performance across various tasks including super-resolution, deblurring, and inpainting, impacting fields like medical imaging and computer vision.