两步相移算法：我们在哪里？
Two-Step Phase Shifting Algorithms: Where Are We?
摘要：
两步相移干涉技术是近年来的研究热点。对12种有代表性的两步相移干涉自校正算法进行了比较研究。我们通过使用3种不同的归一化处理来估计合成和实验条纹图的相位步长来评估这些算法的性能：Gabor滤波器组（GFB），深度神经网络（DNN）和希尔伯特黄变换（HHT）;为了检索背景，幅度调制和噪声。我们通过使用GFB和DNN作为归一化预处理，以及使用中值等鲁棒估计器来估计相位步长，来呈现最先进的相位步长估计算法的变体。我们目前的实验结果比较归一化过程和两步相移算法的组合。我们的研究表明，从两步干涉图的恢复相位的质量是更依赖于归一化过程比相位步长估计方法。
英文摘要：
Two steps phase shifting interferometry has been a hot topic in the recent years. We present a comparison study of 12 representative self--tunning algorithms based on two-steps phase shifting interferometry. We evaluate the performance of such algorithms by estimating the phase step of synthetic and experimental fringe patterns using 3 different normalizing processes: Gabor Filters Bank (GFB), Deep Neural Networks (DNNs) and Hilbert Huang Transform (HHT); in order to retrieve the background, the amplitude modulation and noise. We present the variants of state-of-the-art phase step estimation algorithms by using the GFB and DNNs as normalization preprocesses, as well as the use of a robust estimator such as the median to estimate the phase step. We present experimental results comparing the combinations of the normalization processes and the two steps phase shifting algorithms. Our study demonstrates that the quality of the retrieved phase from of two-step interferograms is more dependent of the normalizing process than the phase step estimation method.
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