Publication detail

Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

NOVOSADOVÁ, M. RAJMIC, P. ŠOREL, M.

Original Title

Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

Type

journal article in Web of Science

Language

English

Original Abstract

Segmentation and denoising of signals often rely on the polynomial model which assumes that every segment is a polynomial of a certain degree and that the segments are modeled independently of each other. Segment borders (breakpoints) correspond to positions in the signal where the model changes its polynomial representation. Several signal denoising methods successfully combine the polynomial assumption with sparsity. In this work, we follow on this and show that using orthogonal polynomials instead of other systems in the model is beneficial when segmenting signals corrupted by noise. The switch to orthogonal bases brings better resolving of the breakpoints, removes the need for including additional parameters and their tuning, and brings numerical stability. Last but not the least, it comes for free!

Keywords

Signal segmentation; Signal smoothing; Signal approximation; Denoising; Piecewise polynomials; Orthogonality; Sparsity; Proximal splitting; Convex optimization

Authors

NOVOSADOVÁ, M.; RAJMIC, P.; ŠOREL, M.

Released

25. 1. 2019

Publisher

Springer Open

ISBN

1687-6172

Periodical

EURASIP Journal on Advances in Signal Processing

Year of study

2019

Number

6

State

United States of America

Pages from

1

Pages to

15

Pages count

15

URL

http://link.springer.com/article/10.1186/s13634-018-0598-9

Full text in the Digital Library

http://hdl.handle.net/11012/137441

BibTex

@article{BUT153383,
  author="Michaela {Novosadová} and Pavel {Rajmic} and Michal {Šorel}",
  title="Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising",
  journal="EURASIP Journal on Advances in Signal Processing",
  year="2019",
  volume="2019",
  number="6",
  pages="1--15",
  doi="10.1186/s13634-018-0598-9",
  issn="1687-6172",
  url="http://link.springer.com/article/10.1186/s13634-018-0598-9"
}