Pandit S M, Jordache N
Appl Opt. 1995 Sep 10;34(26):5945-51. doi: 10.1364/AO.34.005945.
Results of wave-front phase detection obtained from a spatial method based on data-dependent-systems (DDS) methodology are compared with those obtained from the Fourier-transform method. DDS is a novel approach that extends and improves the way the stochastic autoregressive moving-average models are obtained and interpreted. The methodology is robust to noise influence and insensitive to the errors commonly associated with the Fourier transform. Both the Fourier-transform and the DDS methods use one interference pattern, and both offer means for filtering out disturbances such as noise and background variations. We present a brief review of the two methods to compare them theoretically, and then we describe their experimental implementation. The methods were applied to the same interferometric data sets, and the results are presented and compared to discuss relative advantages and disadvantages. In particular, it is shown that the DDS method preserves the detailed surface texture because a convolution of the component that represents the surface dynamic aspect with the component that corresponds to the independent and dynamic-free aspect is able to recover the original details. In contrast the Fourier-transform method smooths such details to an extent that depends on the subjective choice of filters.
将基于数据相关系统（DDS）方法的空间方法获得的波前相位检测结果与傅里叶变换方法获得的结果进行比较。DDS是一种新颖的方法，它扩展并改进了随机自回归移动平均模型的获取和解释方式。该方法对噪声影响具有鲁棒性，并且对通常与傅里叶变换相关的误差不敏感。傅里叶变换方法和DDS方法都使用一个干涉图样，并且都提供了滤除噪声和背景变化等干扰的手段。我们对这两种方法进行简要回顾以便从理论上对它们进行比较，然后描述它们的实验实现。这两种方法被应用于相同的干涉数据集，并展示和比较结果以讨论相对优缺点。特别地，结果表明DDS方法保留了详细的表面纹理，因为代表表面动态方面的分量与对应于独立且无动态方面的分量的卷积能够恢复原始细节。相比之下，傅里叶变换方法会将此类细节平滑到一定程度，这取决于滤波器的主观选择。
