Image reconstruction with imperfect forward models and applications in deblurring

Author
Korolev, Yury · Lellmann, Jan
Year 2017
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Abstract

We present and analyse an approach to image reconstruction problems with imperfect forward models based on partially ordered spaces - Banach lattices. In this approach, errors in the data and in the forward models are described using order intervals. The method can be characterised as the lattice analogue of the residual method, where the feasible set is defined by linear inequality constraints. The study of this feasible set is the main contribution of this paper. Convexity of this feasible set is examined in several settings and modifications for introducing additional information about the forward operator are considered. Numerical examples demonstrate the performance of the method in deblurring with errors in the blurring kernel.

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Details

Title
Image reconstruction with imperfect forward models and applications in deblurring
Author
Korolev, Yury · Lellmann, Jan
Year
2017
Type
Research Article
Language
eng
History*
2017-08-03 00:00:00 · 2017-08-07 00:00:00
Categories
Computer Vision and Pattern Recognition · Numerical Analysis

Fields edited by Q-Sensei or Q-Sensei's users are marked with an asterisk (*).
This is Version 2 of this record. Q-Sensei Corp. added this version on August 9, 2017. It is an edited version of the original data import from arXiv.org e-Print archive. View changes to the previous version or view the complete version history.