Sparse fast Clifford Fourier transform
Sparse fast Clifford Fourier transform
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DOI：
10.1631/fitee.1500452
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发表时间：
2017-10
影响因子：
3
通讯作者：
Rui Wang;Yihang Zhou;Yanliang Jin;W. Cao
中科院分区：
文献类型：
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作者：
Rui Wang;Yihang Zhou;Yanliang Jin;W. Cao
Clifford傅立叶变换(CFT)既可以应用于矢量场，也可以应用于标量场。然而，由于大数据的问题，CFT效率不高，因为算法是在每个信号量中计算的。稀疏快速傅立叶变换(SFFT)理论通过选择性地使用输入数据来处理大数据问题。这启发了我们提出了一种新的算法，称为稀疏快速CFT(SFCFT)，它可以极大地提高标量场和矢量场的计算性能。使用标量场、灰度和彩色图像进行了实验，并与使用FFT、CFT和sFFT的结果进行了比较。结果表明，SFCFT可以有效地提高多矢量信号处理的性能。
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.