Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging

Understand

Computational image reconstruction algorithms generally produce a single image without any measure of uncertainty or confidence.

• Regularized Maximum Likelihood (RML) and feed-forward deep learning approaches for inverse problems typically focus on recovering a point estimate.
• This is a serious limitation when working with underdetermined imaging systems, where it is conceivable that multiple image modes would be consistent with the measured data.
• Characterizing the space of probable images that explain the observational data is therefore crucial.