6 citations · 13 across the 13 of their papers we have counts for
13 papers
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)…
Sparse Bayesian Inference with Regularized Gaussian Distributions
Jasper Marijn Everink, Yiqiu Dong, Martin Skovgaard Andersen
Regularization is a common tool in variational inverse problems to impose assumptions on the parameters of the problem. One such assumption is sparsity, which is commonly promoted…
Bayesian Inference with Projected Densities
Jasper Marijn Everink, Yiqiu Dong, Martin Skovgaard Andersen
Constraints are a natural choice for prior information in Bayesian inference. In various applications, the parameters of interest lie on the boundary of the constraint set. In this…
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…
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…
A hybrid Gibbs sampler for edge-preserving tomographic reconstruction with uncertain view angles
Felipe Uribe, Johnathan M. Bardsley, Yiqiu Dong +2
In computed tomography, data consist of measurements of the attenuation of X-rays passing through an object. The goal is to reconstruct the linear attenuation coefficient of the ob…
A fast method for simultaneous reconstruction and segmentation in X-ray CT application
Yiqiu Dong, Chunlin Wu, Shi Yan
In this paper, we propose a fast method for simultaneous reconstruction and segmentation (SRS) in X-ray computed tomography (CT). Our work is based on the SRS model where Bayes' ru…
Fixing Nonconvergence of Algebraic Iterative Reconstruction with an Unmatched Backprojector
Yiqiu Dong, Per Christian Hansen, Michiel E. Hochstenbach +1
We consider algebraic iterative reconstruction methods with applications in image reconstruction. In particular, we are concerned with methods based on an unmatched projector/backp…
Tomographic Reconstruction with Spatially Varying Parameter Selection
Yiqiu Dong, Carola-Bibiane Schönlieb
In this paper we propose a new approach for tomographic reconstruction with spatially varying regularization parameter. Our work is based on the SA-TV image restoration model propo…
Tomographic Reconstruction Methods for Decomposing Directional Components
Rasmus Dalgas Kongskov, Yiqiu Dong
Decomposition of tomographic reconstructions has many different practical application. We propose two new reconstruction methods that combines the task of tomographic reconstructio…
Directional Total Generalized Variation Regularization
Rasmus Dalgas Kongskov, Yiqiu Dong, Kim Knudsen
In inverse problems, prior information and a priori-based regularization techniques play important roles. In this paper, we focus on image restoration problems, especially on resto…
Bregman Cost for Non-Gaussian Noise
Martin Burger, Yiqiu Dong, Federica Sciacchitano
One of the tasks of the Bayesian inverse problem is to find a good estimate based on the posterior probability density. The most common point estimators are the conditional mean (C…
Exact Relaxation for Classes of Minimization Problems with Binary Constraints
Martin Burger, Yiqiu Dong, Michael Hintermüller
Relying on the co-area formula, an exact relaxation framework for minimizing objectives involving the total variation of a binary valued function (of bounded variation) is presente…