Navigating the Manifold — A Geometric Perspective on Diffusion-Based Inverse Problems

摘要

This blogpost develops a geometric and probabilistic lens on diffusion priors for inverse problems. We show that a wide range of methods mostly instantiate two operator-splitting paradigms, i.e., posterior-guided sampling and clean-space local-MAP optimization. Through manifold diagrams, Tweedie-based animations, and step-by-step derivations, we explain how these paradigms decouple a pretrained diffusion prior from measurement physics, clarify when they approximate full posterior sampling versus MAP estimation, and distill practical design rules for building robust diffusion-based inverse solvers.