Wunsch Ii D C, Marks Ii R J, Caudell T P, Capps C D
Appl Opt. 1992 Sep 10;31(26):5681-7. doi: 10.1364/AO.31.005681.
The feasibility of using certain types of binary phase-only filter (BPOF) is investigated. A critical aspect of correlation filters is not often addressed in research on BPOF's: how well do they perform as classifiers in the presence of imperfectly matching templates? It is not enough to detect a single given signal in the presence of noise; it is equally critical to make the correct classification among a number of possible templates with a low false-alarm rate. We show that (+1, -1)-valued BPOF's based on the real part of a conventional matched filter can cause misclassification of simple patterns, even in the absence of noise. These are known to be suboptimal, but the seriousness of their limitations illustrates an important design issue. It is therefore concluded that other types of filters must be used for correlator-based neural network implementations and image processing in general. We also include a commentary on the potential for facing this type of problem with general POF's and BPOF's. The theoretical results are supported by computer simulation and optical experiments.
研究了使用某些类型的二元纯相位滤波器（BPOF）的可行性。在BPOF的研究中，相关滤波器的一个关键方面常常未被涉及：在模板匹配不完全的情况下，它们作为分类器的性能如何？在有噪声的情况下检测单个给定信号是不够的；在多个可能的模板中以低误报率进行正确分类同样至关重要。我们表明，基于传统匹配滤波器实部的（+1，-1）值BPOF即使在没有噪声的情况下也可能导致简单模式的误分类。这些已知是次优的，但其局限性的严重性说明了一个重要的设计问题。因此得出结论，一般来说，其他类型的滤波器必须用于基于相关器的神经网络实现和图像处理。我们还对通用POF和BPOF面对这类问题的可能性进行了评论。理论结果得到了计算机模拟和光学实验的支持。
