Building on the well-known total variation, this paper develops a general regularization technique based on nonlinear isotropic diffusion (NID) for inverse problems with piecewise smooth solutions. The novelty of our approach is to be adaptive (we speak of A-NID), i.e., the regularization varies during the iterates in order to incorporate prior information on the edges, deal with the evolution of the reconstruction and circumvent the limitations due to the nonconvexity of the proposed functionals. After a detailed analysis of the convergence and well-posedness of the method, the latter is validated by simulations performed on synthetic and real data on computerized tomography.
%0 Journal Article
%1 10.1093/imanum/drad002
%A Hahn, Bernadette N
%A Rigaud, Gaël
%A Schmähl, Richard
%D 2023
%J IMA Journal of Numerical Analysis
%K from:carolastahl
%R 10.1093/imanum/drad002
%T A class of regularizations based on nonlinear isotropic diffusion for inverse problems
%U https://doi.org/10.1093/imanum/drad002
%X Building on the well-known total variation, this paper develops a general regularization technique based on nonlinear isotropic diffusion (NID) for inverse problems with piecewise smooth solutions. The novelty of our approach is to be adaptive (we speak of A-NID), i.e., the regularization varies during the iterates in order to incorporate prior information on the edges, deal with the evolution of the reconstruction and circumvent the limitations due to the nonconvexity of the proposed functionals. After a detailed analysis of the convergence and well-posedness of the method, the latter is validated by simulations performed on synthetic and real data on computerized tomography.
@article{10.1093/imanum/drad002,
abstract = {{Building on the well-known total variation, this paper develops a general regularization technique based on nonlinear isotropic diffusion (NID) for inverse problems with piecewise smooth solutions. The novelty of our approach is to be adaptive (we speak of A-NID), i.e., the regularization varies during the iterates in order to incorporate prior information on the edges, deal with the evolution of the reconstruction and circumvent the limitations due to the nonconvexity of the proposed functionals. After a detailed analysis of the convergence and well-posedness of the method, the latter is validated by simulations performed on synthetic and real data on computerized tomography.}},
added-at = {2023-03-30T12:13:17.000+0200},
author = {Hahn, Bernadette N and Rigaud, Gaël and Schmähl, Richard},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/20c5ab2bb1841f0df1cef81bab5d3cf99/mathematik},
doi = {10.1093/imanum/drad002},
eprint = {https://academic.oup.com/imajna/advance-article-pdf/doi/10.1093/imanum/drad002/49332831/drad002.pdf},
interhash = {ec0c2ad15f55791c070b356fd971816b},
intrahash = {0c5ab2bb1841f0df1cef81bab5d3cf99},
issn = {0272-4979},
journal = {IMA Journal of Numerical Analysis},
keywords = {from:carolastahl},
month = {02},
note = {drad002},
timestamp = {2023-03-30T12:13:17.000+0200},
title = {A class of regularizations based on nonlinear isotropic diffusion for inverse problems},
url = {https://doi.org/10.1093/imanum/drad002},
year = 2023
}
