丈中利用双梯度算法对自然图像的基向量进行迭代学习。
The basis vectors of the natural images were obtained by using fast conjugate gradient algorithm.
计算量比较结果显示，频域抽取多维向量基FFT算法比多维分离式FFT算法计算量低。
The comparison results show that, compared with multi-dimensional separable FFT, the DIF multi-dimensional vector radix FFT algorithm has lower calculation load.
该FFT算法适合于维数为任意整数的情况，当维数为1时，算法退化为著名的频域抽取向量基2 FFT算法。
This FFT algorithm can be used with arbitrary integer dimensions. For 1-dimension, the algorithm will be simplified as the well-known DIF vector radix 2 FFT.
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