Guided Restoration

Guided restoration focuses on improving image quality by reversing various degradations, such as noise, blur, and low-light conditions, using advanced computational methods. Current research emphasizes leveraging large pre-trained models, particularly diffusion models and transformers, often incorporating prompt-based guidance to control the restoration process and handle multiple degradation types simultaneously. This work aims to create more efficient and generalizable restoration techniques, impacting diverse fields like medical imaging, remote sensing, and digital art by enabling high-quality image reconstruction from imperfect data.