论文标题
两步阶段转移算法：我们在哪里？
Two-Step Phase Shifting Algorithms: Where Are We?
论文作者
论文摘要
近年来，两步阶段转移干涉法一直是一个热门话题。我们提出了基于两步相位转移干涉仪的12种代表性自我效果算法的比较研究。我们通过使用3种不同的标准化过程估算合成和实验条纹模式的相位步骤来评估此类算法的性能：Gabor过滤器库（GFB），深神经网络（DNNS）和Hilbert Huang Transform（HHT）；为了检索背景，振幅调制和噪声。我们通过使用GFB和DNN作为归一化预处理以及使用强大的估计器（例如中位数）来估算阶段步骤的使用来介绍最新的阶段步骤估计算法的变体。我们提出了比较归一化过程和两个步骤相移算法的组合的实验结果。我们的研究表明，从两步干涉图中检索到的阶段的质量比阶段估计方法更依赖于标准化过程。
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.
