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A refined denoising method for noisy phase-shifting interference fringe patterns

机译：一种嘈杂相移干涉条纹图案的精制去噪方法

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摘要

Denoising process is indispensable procedure to get accurate wrapped and unwrapped phase maps from noisy phase-shifting interference fringe patterns. The efficiency of this process has high correlation with the classification accuracy of the noise type. Usually, the classification process for the noise type in the fringe patterns is performed via the experience which may increase the error level. In this paper, a denoising method based on a refined pre-trained deep learning networks is proposed. This method can perform an automatic denoising for the noisy fringe patterns according to its type. So that, the pre-classification process for the noise type is the core of this method. A dataset containing 902 numerical stimulated interference fringe patterns is established. This dataset includes four classes; no noise, salt and pepper noise, Gaussian noise and speckle noise. To perform the automatic classification process, a pre-trained Alex CNN is fine-tuned. This network achieved 97.5% validation accuracy and 98% testing accuracy. To improve the efficiency, an incorporation between the AlexNet and the support vector machine classifier is proposed. After the classification process, each noisy interference fringe pattern is treated according to its class. The proposed method is applied on realistic noisy interference patterns for isotactic polypropylene (iPP) and nylon 6 fibres captured using the phase-shifting interference microscope. The phase distribution values and the 3D birefringence are calculated for iPP and nylon 6 fibres. Our experimental results show that our proposed method achieves high efficiency in the denoising process.
机译：去噪过程是从嘈杂的相移干涉条纹图案获得准确的包裹和未包装的相位图的必不可少的过程。该过程的效率与噪声类型的分类精度具有高的相关性。通常，通过可以增加误差水平的经验来执行条纹图案中的噪声类型的分类过程。本文提出了一种基于精细预训练的深学习网络的去噪方法。该方法可以根据其类型对噪声边缘图案进行自动去噪。这样，噪声类型的预分类过程是该方法的核心。建立包含902个数值刺激干涉条纹图案的数据集。此数据集包含四个类;没有噪音，盐和辣椒噪音，高斯噪音和斑点噪音。为了执行自动分类过程，预先训练的Alex CNN是微调的。该网络实现了97.5％的验证精度和98％的测试精度。为了提高效率，提出了AlexNet和支持向量机分类器之间的结合。在分类过程之后，根据其类处理每种噪声干涉条纹图案。所提出的方法应用于使用相移干扰显微镜捕获的全同立构聚丙烯（IPP）和尼龙6纤维的现实嘈杂干扰图。为IPP和尼龙6纤维计算相位分布值和3D双折射。我们的实验结果表明，我们的提出方法在去噪过程中实现了高效率。

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