Dynamical Sampling with Additive Random Noise
| Autor: | Peter Volgyesi, Akram Aldroubi, Roy R. Lederman, Longxiu Huang, Ilya A. Krishtal, Akos Ledeczi |
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| Rok vydání: | 2018 |
| Předmět: |
Algebra and Number Theory
Noise (signal processing) Computer science Noise reduction 010102 general mathematics Sampling (statistics) Numerical Analysis (math.NA) 010103 numerical & computational mathematics 01 natural sciences Symmetric convolution Action (physics) Linear map Computational Mathematics symbols.namesake Random noise FOS: Mathematics symbols Radiology Nuclear Medicine and imaging Mathematics - Numerical Analysis 0101 mathematics Algorithm Analysis Gibbs sampling |
| Zdroj: | Sampling Theory in Signal and Image Processing. 17:153-182 |
| ISSN: | 1530-6429 |
| DOI: | 10.1007/bf03549662 |
| Popis: | Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamical sampling algorithms in the finite dimensional case and study the impact of additive noise. The algorithms are implemented and tested on synthetic and real data sets, and denoising techniques are integrated to mitigate the effect of the noise. We also develop theoretical and numerical results that validate the algorithm for recovering the driving operators, which are defined via a real symmetric convolution. |
| Databáze: | OpenAIRE |
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