Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Jörg Polzehl, Kostas Papafitsoros, Karsten Tabelow
Title: Patch-Wise Adaptive Weights Smoothing in R
Abstract: Image reconstruction from noisy data has a long history of methodological development and is based on a variety of ideas. In this paper we introduce a new method called patchwise adaptive smoothing, that extends the propagation-separation approach by using comparisons of local patches of image intensities to define local adaptive weighting schemes for an improved balance of reduced variability and bias in the reconstruction result. We present the implementation of the new method in an R package aws and demonstrate its properties on a number of examples in comparison with other state-of-the art image reconstruction methods.

Page views:: 1186. Submitted: 2018-07-30. Published: 2020-10-07.
Paper: Patch-Wise Adaptive Weights Smoothing in R     Download PDF (Downloads: 436)
aws_2.5.tar.gz: R source package Download (Downloads: 25; 621KB) Replication materials Download (Downloads: 16; 11MB)

DOI: 10.18637/jss.v095.i06

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.