ADIR: Adaptive Diffusion for Image Reconstruction

Understand

Denoising diffusion models have recently achieved remarkable success in image generation, capturing rich information about natural image statistics.

• This makes them highly promising for image reconstruction, where the goal is to recover a clean image from a degraded observation.
• In this work, we introduce a conditional sampling framework that leverages the powerful priors learned by diffusion models while enforcing consistency with the available measurements.
• To further adapt pre-trained diffusion models to the specific degradation at hand, we propose a novel fine-tuning strategy.