Fringe patterns encode the information about the result of a measurement performed via widely used optical full-field testing methods, e.g., interferometry, digital holographic microscopy, moiré techniques, structured illumination etc. Affected by the optical setup, changing environment and the sample itself fringe patterns are often corrupted with substantial noise, strong and uneven background illumination and exhibit low contrast. Fringe pattern enhancement, i.e., noise minimization and background term removal, at the pre-processing stage prior to the phase map calculation (for the measurement result decoding) is therefore essential to minimize the jeopardizing effect the mentioned error sources have on the optical measurement outcome. In this contribution we propose an automatic, robust and highly effective fringe pattern enhancement method based on the novel period-guided bidimensional empirical mode decomposition algorithm (PG-BEMD). The spatial distribution of the fringe period is estimated using the novel windowed approach and then serves as an indicator for the truly adaptive decomposition with the filter size locally adjusted to the fringe pattern density. In this way the fringe term is successfully extracted in a single (first) decomposition component alleviating the cumbersome mode mixing phenomenon and greatly simplifying the automatic signal reconstruction. Hence, the fringe term is dissected without the need for modes selection nor summation. The noise removal robustness is ensured employing the block matching 3D filtering of the fringe pattern prior to its decomposition. Performance validation against previously reported modified empirical mode decomposition techniques is provided using numerical simulations and experimental data verifying the versatility and effectiveness of the proposed approach.

中文翻译：

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使用真正的自适应周期导引的二维经验模式分解自动条纹图案增强

条纹图案编码有关通过广泛使用的光学全场测试方法（例如，干涉测量，数字全息显微镜，莫尔条纹技术，结构化照明等）执行的测量结果的信息。受光学设置，变化的环境和样品本身条纹的影响图案经常会因大量噪音，强烈和不均匀的背景照明而损坏，并且显示出低对比度。因此，在相位图计算（用于测量结果解码）之前的预处理阶段，边缘图案增强（即噪声最小化和背景项去除）对于使上述误差源对光学测量结果的危害最小化至关重要。 。在此文稿中，我们建议自动 基于新颖的周期导引的二维经验模式分解算法（PG-BEMD）的鲁棒且高效的条纹图案增强方法。边缘时段的空间分布是使用新颖的窗口方法估算的，然后用作真正自适应分解的指标，滤波器大小局部调整为边缘图案密度。这样，可以在单个（第一）分解分量中成功提取边缘项，从而减轻了繁琐的模式混合现象，并大大简化了自动信号重构。因此，不需要进行模式选择或求和就可以分解边缘项。在条纹图案分解之前，通过对条纹图案进行块匹配3D滤波，可以确保去除噪声的鲁棒性。