61 citations · 151 across the 17 of their papers we have counts for
13 papers · 1 filter
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)…
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…
Stopping Rules for Algebraic Iterative Reconstruction Methods in Computed Tomography
Per Christian Hansen, Jakob Sauer Jørgensen, Peter Winkel Rasmussen
Algebraic models for the reconstruction problem in X-ray computed tomography (CT) provide a flexible framework that applies to many measurement geometries. For large-scale problems…
A twin error gauge for Kaczmarz's iterations
Bart S. van Lith, Per Christian Hansen, Michiel E. Hochstenbach
We propose two new algebraic reconstruction techniques based on Kaczmarz's method that produce a regularized solution to noisy tomography problems. Tomography problems exhibit semi…