Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data

Understand

We present SURE-Score: an approach for learning score-based generative models using training samples corrupted by additive Gaussian noise.

• When a large training set of clean samples is available, solving inverse problems via score-based (diffusion) generative models trained on the underlying fully-sampled data distribution has recently been shown to outperform end-to-end supervised deep learning.
• In practice, such a large collection of training data may be prohibitively expensive to acquire in the first place.
• In this work, we present an approach for approximately learning a score-based generative model of the clean distribution, from noisy training data.