Bayesian Imaging

Bayesian imaging leverages probabilistic frameworks to reconstruct images from incomplete or noisy data, aiming for both high-quality image estimates and reliable uncertainty quantification. Current research emphasizes efficient algorithms like Markov Chain Monte Carlo (MCMC) and variational inference, often incorporating advanced generative models such as diffusion models and normalizing flows as image priors to capture complex image statistics. This approach is crucial for diverse applications, including medical imaging, astronomy, and materials science, where accurate image reconstruction and robust uncertainty assessment are essential for reliable interpretation and decision-making. The field is actively exploring ways to improve computational efficiency and the accuracy of uncertainty quantification in these methods.