Reconstruction Uncertainty

Reconstruction uncertainty, the quantification of error and ambiguity in reconstructing images or 3D models from incomplete or noisy data, is a critical challenge across diverse imaging modalities. Current research focuses on developing probabilistic models, including diffusion models, normalizing flows, and deep generative models, to estimate this uncertainty, often incorporating physics-informed priors or leveraging deep learning architectures like deep image priors. Accurate uncertainty quantification is crucial for improving the reliability of reconstructions in applications ranging from medical imaging and autonomous navigation to materials science and connectomics, enabling more informed decision-making based on the inherent limitations of the reconstruction process.