Listening to the Noise: Blind Denoising with Gibbs Diffusion

Understand

In recent years, denoising problems have become intertwined with the development of deep generative models.

• In particular, diffusion models are trained like denoisers, and the distribution they model coincide with denoising priors in the Bayesian picture.
• However, denoising through diffusion-based posterior sampling requires the noise level and covariance to be known, preventing blind denoising.
• We overcome this limitation by introducing Gibbs Diffusion (GDiff), a general methodology addressing posterior sampling of both the signal and the noise parameters.