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Uncertainty Quantification for Linear Inverse Problems with Besov Prior: A Randomize-Then-Optimize Method
Andreas Horst, Babak Maboudi Afkham, Yiqiu Dong +1
In this work, we investigate the use of Besov priors in the context of Bayesian inverse problems. The solution to Bayesian inverse problems is the posterior distribution which natu…
math.NA2025
Dual-grid parameter choice method with application to image deblurring
Markus Juvonen, Bjørn Jensen, Ilmari Pohjola +2
Variational regularization of ill-posed inverse problems is based on minimizing the sum of a data fidelity term and a regularization term. The balance between them is tuned using a…