2013
DOI: 10.1007/s10851-013-0430-y
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Convolution Products for Hypercomplex Fourier Transforms

Abstract: Hypercomplex Fourier transforms are increasingly used in signal processing for the analysis of higher-dimensional signals such as color images. A main stumbling block for further applications, in particular concerning filter design in the Fourier domain, is the lack of a proper convolution theorem. The present paper develops and studies two conceptually new ways to define convolution products for such transforms. As a by-product, convolution theorems are obtained that will enable the development and fast imple… Show more

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Cited by 25 publications
(22 citation statements)
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“…This is rather surprising, as in all the other examples of hypercomplex Fourier transforms (see e.g. [4]), the transform is only diagonalized by one basis and not by both.…”
Section: The Spherical Basismentioning
confidence: 96%
See 2 more Smart Citations
“…This is rather surprising, as in all the other examples of hypercomplex Fourier transforms (see e.g. [4]), the transform is only diagonalized by one basis and not by both.…”
Section: The Spherical Basismentioning
confidence: 96%
“…Examples of Fourier transforms for which eigenfunctions are used to construct them, can be found in e.g. [1,2,3,4,11,12,13]. The main issue that hinders further development of applications is the lack of a suitable convolution theorem for such hypercomplex Fourier transforms.…”
Section: Introductionmentioning
confidence: 99%
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“…We may now formulate the following theorem, see [6], which expresses the Mustard convolution as a finite linear combination of classical convolutions: f * g + f * g (0,1) + f * g (1,0) + f * g (1,1)…”
Section: The Quaternion Fourier Transformmentioning
confidence: 99%
“…The key idea to solve this problem is to find a suitable decomposition of the two signals to be convolved, so that the action of the qFT simplifies to a sum of products. This will be tackled using a mathematical tool we call Mustard convolution, introduced in [6] as follows:…”
Section: Introductionmentioning
confidence: 99%