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Fast fractional fourier transforms based on infinitesimal fourier transform

Authors: Labunets, V. G.; Artemov, I.; Martyugin, S.; Ostheimer, E.;

Fast fractional fourier transforms based on infinitesimal fourier transform

Abstract

Дробные преобразования Фурье (ДрПФ) формируют однопараметрическое семейство унитарных преобразований. Они нашли многочисленные применения в обработке сигналов и изображений. Тождественное и классическое преобразования Фурье – частные случаи ДрПФ. До настоящего времени дробные спектры Фурье вычислялись на основе классического быстрого преобразования Фурье. Этот метод отображает N отсчетов исходной функции f в MN спектральных отсчетов M-элементного множества спектров, что требует MN(2+logN) умножений и MNlogN сложений. В данной работе предлагается новый численный алгоритм расчета, требующий 2MN умножений и 3MN сложений и который основывается на инфинитезимальном преобразовании Фурье. The fractional Fourier transforms (FrFTs) is one-parametric family of unitary transformations. FrFTs found a lot of applications in signal and image processing. The identical and classical Fourier transformations are both the special cases of the FrFTs. Up to now, the fractional Fourier spectra, has been digitally computed using classical approach based on the fast discrete Fourier transform. This method maps the N samples of the original function f to the N samples of the M-element set of spectra, which requires MN(2+logN) multiplications and MNlogN additions. This paper develops a new numerical algorithm, which requires 2MN multiplications and 3MN additions and which is based on the infinitesimal Fourier transform.

Keywords

ОБРАБОТКА СИГНАЛОВ И ИЗОБРАЖЕНИЙ, ОПЕРАТОР ШРЕДИНГЕРА, БЫСТРОЕ ДРОБНОЕ ПРЕОБРАЗОВАНИЕ ФУРЬЕ, 540, ИНФИНИТЕЗИМАЛЬНОЕ ПРЕОБРАЗОВАНИЕ ФУРЬЕ, 510

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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