Evers, L. and Heaton, T.J. (2017) Locally adaptive tree-based thresholding using the treethresh package in R. Journal of Statistical Software, 78. Code Snipp. ISSN 1548-7660
Abstract
This paper introduces the treethresh package offering accurate estimation, via thresholding, of potentially sparse heterogeneous signals and the denoising of images using wavelets. It gives considerably improved performance over other estimation methods if the underlying signal or image is not homogeneous throughout but instead has distinct regions with differing sparsity or strength characteristics. It aims to identify these different regions and perform separate estimation in each accordingly. The base algorithm offers code which can be applied directly to any one-dimensional potentially sparse sequence observed subject to noise. Also included are functions which allow two-dimensional images to be denoised following transformation to the wavelet domain. In addition to reconstructing the underlying signal or image, the package provides information on the believed partitioning of the signal or image into its differing regions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 The Author(s). This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License. |
Keywords: | CARTs; wavelets; thresholding; sparsity; denoising; heterogeneous; partition |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Aug 2016 11:09 |
Last Modified: | 20 Oct 2023 10:12 |
Status: | Published |
Publisher: | University of California, Los Angeles |
Refereed: | Yes |
Identification Number: | 10.18637/jss.v078.c02 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:103219 |