Self-diffusion for Solving Inverse Problems
bayesian perspectiveconvolutional networkdata fidelity lossdenoisingdiffusion-based approachesinverse problemsiterative processlinear inverse problemsnoisingposterior samplingpretrained generative modelsscheduled noise processself-denoiserself-diffusionspectral bias
We propose ***self-diffusion***, a novel framework for solving inverse problems without relying on pretrained generative models. Traditional diffusion-based approaches require training a model on a clean dataset to learn to reverse the forward noising process. This model is then used to sample clean solutions