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Frontiers of Information Technology & Electronic Engineering
ISSN 2095-9184 (print), ISSN 2095-9230 (online)
2017 Vol.18 No.8 P.1131-1141
Sparse fast Clifford Fourier transform
Abstract: The Clifford Fourier transform (CFT) can be applied to both vector and scalar fields. However, due to problems with big data, CFT is not efficient, because the algorithm is calculated in each semaphore. The sparse fast Fourier transform (sFFT) theory deals with the big data problem by using input data selectively. This has inspired us to create a new algorithm called sparse fast CFT (SFCFT), which can greatly improve the computing performance in scalar and vector fields. The experiments are implemented using the scalar field and grayscale and color images, and the results are compared with those using FFT, CFT, and sFFT. The results demonstrate that SFCFT can effectively improve the performance of multivector signal processing.
Key words: Sparse fast Fourier transform (sFFT); Clifford Fourier transform (CFT); Sparse fast Clifford Fourier transform (SFCFT); Clifford algebra
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DOI:
10.1631/FITEE.1500452
CLC number:
TP391
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On-line Access:
2017-09-08
Received:
2015-12-09
Revision Accepted:
2016-08-23
Crosschecked:
2017-08-24