Adaptive Regularization for Image Reconstruction from Subsampled Data

M. Hintermüller, A. Langer, C.N. Rautenberg, T. Wu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

Choices of regularization parameters are central to variational methods for image restoration . In this paper, a spatially adaptive (or distributed) regularization scheme is developed based on localized residuals, which properly balances the regularization weight between regions containing image details and homogeneous regions. Surrogate iterative methods are employed to handle given subsampled data in transformed domains, such as Fourier or wavelet data. In this respect, this work extends the spatially variant regularization technique previously established in Dong et al. (J Math Imaging Vis 40:82–104, 2011), which depends on the fact that the given data are degraded images only. Numerical experiments for the reconstruction from partial Fourier data and for wavelet inpainting prove the efficiency of the newly proposed approach.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Imaging, Vision and Learning Based Optimization and PDEs. ILVOPDE 2016
PublisherSpringer, Cham
Pages3-26
Number of pages24
ISBN (Electronic)978-3-319-91274-5
ISBN (Print)978-3-319-91273-8
DOIs
Publication statusPublished - 20 Nov 2018
Externally publishedYes
EventInternational Conference on Imaging, Vision and Learning based on Optimization and PDEs - Bergen, Norway
Duration: 29 Aug 20162 Sep 2016

Conference

ConferenceInternational Conference on Imaging, Vision and Learning based on Optimization and PDEs
Abbreviated titleIVLOPDE 2016
CountryNorway
CityBergen
Period29/08/162/09/16

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