The FOURIER transform is one of the most frequently used tools in signal analysis. A generalization of the Fourier transform-the fractional Fourier transform-has become a powerful tool for time-varying signal analys...The FOURIER transform is one of the most frequently used tools in signal analysis. A generalization of the Fourier transform-the fractional Fourier transform-has become a powerful tool for time-varying signal analysis. The mean square error(MSE) is used as design criteria to estimate signal. Wiener filter, which can be implement in O(NlogN) time, is suited for time-invariant degradation models. For time-variant and non-stationary processes, however, the linear estimate requires O(N 2 ). Filtering in fractional Fourier domains permits reduction of the error compared with ordinary Fourier domain filtering while requiring O(NlogN) implementation time. The blurred images that have several degradation models with different SNR are restored in the experiments. The results show that the method presented in this paper is valid and that the effect of restoration is improved as SNR is increased.展开更多
文摘The FOURIER transform is one of the most frequently used tools in signal analysis. A generalization of the Fourier transform-the fractional Fourier transform-has become a powerful tool for time-varying signal analysis. The mean square error(MSE) is used as design criteria to estimate signal. Wiener filter, which can be implement in O(NlogN) time, is suited for time-invariant degradation models. For time-variant and non-stationary processes, however, the linear estimate requires O(N 2 ). Filtering in fractional Fourier domains permits reduction of the error compared with ordinary Fourier domain filtering while requiring O(NlogN) implementation time. The blurred images that have several degradation models with different SNR are restored in the experiments. The results show that the method presented in this paper is valid and that the effect of restoration is improved as SNR is increased.
