61 citations · 151 across the 17 of their papers we have counts for
21 papers
CUQIpy: I. Computational uncertainty quantification for inverse problems in Python
Nicolai A B Riis, Amal M A Alghamdi, Felipe Uribe +4
This paper introduces CUQIpy, a versatile open-source Python package for computational uncertainty quantification (UQ) in inverse problems, presented as Part I of a two-part series…
Inferring Object Boundaries and their Roughness with Uncertainty Quantification
Babak Maboudi Afkham, Nicolai André Brogaard Riis, Yiqiu Dong +1
This work describes a Bayesian framework for reconstructing the boundaries that represent targeted features in an image, as well as the regularity (i.e., roughness vs. smoothness)…
Horseshoe priors for edge-preserving linear Bayesian inversion
Felipe Uribe, Yiqiu Dong, Per Christian Hansen
In many large-scale inverse problems, such as computed tomography and image deblurring, characterization of sharp edges in the solution is desired. Within the Bayesian approach to…
Iterative image reconstruction for CT with unmatched projection matrices using the generalized minimal residual algorithm
Emil Y. Sidky, Per Christian Hansen, Jakob S. Jørgensen +1
The generalized minimal residual (GMRES) algorithm is applied to image reconstruction using linear computed tomography (CT) models. The GMRES algorithm iteratively solves square, n…
GMRES Methods for Tomographic Reconstruction with an Unmatched Back Projector
Per Christian Hansen, Ken Hayami, Keiichi Morikuni
Unmatched pairs of forward and back projectors are common in X-ray CT computations for large-scale problems; they are caused by the need for fast algorithms that best utilize the c…
Uncertainty Quantification of Inclusion Boundaries in the Context of X-ray Tomography
Babak Maboudi Afkham, Yiqiu Dong, Per Christian Hansen
In this work, we describe a Bayesian framework for reconstructing the boundaries of piecewise smooth regions in the X-ray computed tomography (CT) problem in an infinite-dimensiona…