Image Restoration Model

Image restoration models aim to recover high-quality images from degraded versions, addressing issues like noise, blur, and compression artifacts. Current research emphasizes developing "all-in-one" models capable of handling multiple degradation types simultaneously, often employing architectures like U-Nets and Transformers, sometimes enhanced with techniques such as prompt learning and model scaling for improved efficiency and performance. These advancements are significant for various applications, including enhancing digital archives, improving medical imaging, and advancing computer vision tasks reliant on high-quality images. The field is also actively exploring methods to improve generalization to unseen degradation types and to quantify uncertainty in restoration results.