Inverse problem

Diffusion Posterior Sampling for General Noisy Inverse Problems

Diffusion posterior sampling enables solving arbitrary noisy (e.g. Gaussian, Poisson) inverse problems that are both linear or non-linear.

Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models

We propose a method that can solve 3D inverse problems in the medical imaging domain using only the pre-trained 2D diffusion model augmented with the conventional model-based prior.

Improving Diffusion Models for Inverse Problems using Manifold Constraints

Manifold constraint dramatically improves the performance of unsupervised inverse problem solving using diffusion models.