Variational Regularization of Inverse Problems for Manifold-Valued Data

04/27/2018
by   Martin Storath, et al.
0

In this paper, we consider the variational regularization of manifold-valued data in the inverse problems setting. In particular, we consider TV and TGV regularization for manifold-valued data with indirect measurement operators. We provide results on the well-posedness and present algorithms for a numerical realization of these models in the manifold setup. Further, we provide experimental results for synthetic and real data to show the potential of the proposed schemes for applications.

READ FULL TEXT

page 23

page 24

page 25

research
08/01/2018

Wavelet Sparse Regularization for Manifold-Valued Data

In this paper, we consider the sparse regularization of manifold-valued ...
research
09/19/2019

Non-smooth variational regularization for processing manifold-valued data

Many methods for processing scalar and vector valued images, volumes and...
research
12/08/2021

Variational Regularization in Inverse Problems and Machine Learning

This paper discusses basic results and recent developments on variationa...
research
02/04/2020

A perturbed collage theorem and its application to inverse interval integral problems

This paper deals with inverse problems subject to imprecise or vague inf...
research
07/19/2023

Weighted inhomogeneous regularization for inverse problems with indirect and incomplete measurement data

Regularization promotes well-posedness in solving an inverse problem wit...
research
02/10/2022

Characterizations of Adjoint Sobolev Embedding Operators for Inverse Problems

We consider the Sobolev embedding operator E_s : H^s(Ω) → L_2(Ω) and its...
research
10/18/2020

Inverse Problem for Dynamic Computer Simulators via Multiple Scalar-valued Contour Estimation

In this paper we consider a dynamic computer simulator that produces a t...

Please sign up or login with your details

Forgot password? Click here to reset