Detail publikačního výsledku

Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

NOVOSADOVÁ, M.; RAJMIC, P.; ŠOREL, M.

Originální název

Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

Anglický název

Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

Druh

Článek WoS

Originální abstrakt

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!

Anglický abstrakt

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!

Klíčová slova

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

Klíčová slova v angličtině

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

Autoři

NOVOSADOVÁ, M.; RAJMIC, P.; ŠOREL, M.

Rok RIV

2019

Vydáno

25.01.2019

Nakladatel

Springer Open

ISSN

1687-6172

Periodikum

EURASIP Journal on Advances in Signal Processing

Svazek

2019

Číslo

6

Stát

Spojené státy americké

Strany od

1

Strany do

15

Strany počet

15

URL

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

Plný text v Digitální knihovně

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"
}

Dokumenty

Novosadova_Rajmic_Sorel-Orthogonality_is_Superiority