Denoising Diffusion Restoration Models

Understand

Many interesting tasks in image restoration can be cast as linear inverse problems.

• A recent family of approaches for solving these problems uses stochastic algorithms that sample from the posterior distribution of natural images given the measurements.
• However, efficient solutions often require problem-specific supervised training to model the posterior, whereas unsupervised methods that are not problem-specific typically rely on inefficient iterative methods.
• This work addresses these issues by introducing Denoising Diffusion Restoration Models (DDRM), an efficient, unsupervised posterior sampling method.