Gocłowski Paweł, Trusiak Maciej, Ahmad Azeem, Styk Adam, Mico Vicente, Ahluwalia Balpreet S, Patorski Krzysztof
Opt Express. 2020 Mar 2;28(5):6277-6293. doi: 10.1364/OE.382543.
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.
条纹图案编码了通过广泛使用的光学全场测试方法（例如干涉测量法、数字全息显微镜、莫尔技术、结构照明等）执行的测量结果的信息。受光学设置、不断变化的环境和样品本身的影响，条纹图案经常被大量噪声、强烈且不均匀的背景照明所破坏，并且对比度较低。因此，在相位图计算（用于测量结果解码）之前的预处理阶段进行条纹图案增强，即最小化噪声和去除背景项，对于最小化上述误差源对光学测量结果的危害至关重要。在本论文中，我们提出了一种基于新型周期引导二维经验模态分解算法（PG-BEMD）的自动、稳健且高效的条纹图案增强方法。使用新型加窗方法估计条纹周期的空间分布，然后将其用作真正自适应分解的指标，滤波器大小会根据条纹图案密度进行局部调整。通过这种方式，条纹项在单个（第一个）分解分量中成功提取，减轻了繁琐的模态混合现象，并大大简化了自动信号重建。因此，无需进行模态选择或求和即可剖析条纹项。在条纹图案分解之前，采用块匹配三维滤波确保了噪声去除的稳健性。使用数值模拟和实验数据针对先前报道的改进经验模态分解技术进行了性能验证，验证了所提方法的通用性和有效性。
