Uncertainty quantification for deep unrolling-based computational imaging
Deep unrolling is an emerging deep learning-based image reconstruction methodology that
bridges the gap between model-based and purely deep learning-based image …
bridges the gap between model-based and purely deep learning-based image …
Quantifying generative model uncertainty in posterior sampling methods for computational imaging
The idea of using generative models to perform posterior sampling for imaging inverse
problems has elicited attention from the computational imaging community. The main …
problems has elicited attention from the computational imaging community. The main …
What does your computational imaging algorithm not know?: A Plug-and-Play model quantifying model uncertainty
Plug-and-Play is an algorithmic framework developed to solve image recovery problems.
Thanks to the empirical success of convolutional neural network (CNN) denoisers …
Thanks to the empirical success of convolutional neural network (CNN) denoisers …