Generative Diffusion Prior

Generative diffusion priors leverage pre-trained diffusion models to enhance various image processing tasks, aiming to improve image quality and address challenges like limited data or unknown degradation types. Current research focuses on adapting these priors for specific applications, such as 3D reconstruction, human rendering, and remote sensing image super-resolution, often incorporating techniques like score-distillation sampling and conditional guidance to improve performance and control. This approach offers a powerful, flexible framework for image restoration and enhancement, impacting diverse fields by enabling high-quality image generation and reconstruction even under challenging conditions.