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Sparse fast Clifford Fourier transform

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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.

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China

    Rui Wang, Yi-xuan Zhou & Yan-liang Jin

  2. College of Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Wen-ming Cao

  3. Department of Electrical and Computer Engineering, University of Missouri, Columbia, 65211, USA

    Wen-ming Cao

Authors
  1. Rui Wang
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  2. Yi-xuan Zhou
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  3. Yan-liang Jin
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  4. Wen-ming Cao
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Correspondence to Wen-ming Cao.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 61301027, 61375015, and 11274226)

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Wang, R., Zhou, Yx., Jin, Yl. et al. Sparse fast Clifford Fourier transform. Frontiers Inf Technol Electronic Eng 18, 1131–1141 (2017). https://doi.org/10.1631/FITEE.1500452

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