Ptychography has become a popular computational imaging method for microscopy in recent years. In the present work we employ a wavelength scanning ptychography technique enhanced by neural networks for imaging with a fiber endoscope. Illumination of the object at various wavelengths is achieved using a single mode fiber, while a multicore fiber collects diffracted light from a distance. Using a U-Net multilayer convolutional neural network, the diffraction pattern is recovered at the far end of the multicore fiber from the recorded intensity pattern at the proximal end. With the recovered diffraction pattern in place, the phase object can be reconstructed using the ptychography algorithm. The quality of the object reconstruction improves with the number of wavelengths used. Comparison with an end-to-end neural network highlights the effectiveness and practicality of this two-step hybrid system. This alternative and simplified ptychographic endoscopy setup delivers noticeable improvements through neural networks and wavelength scanning.

中文翻译：

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使用光纤内窥镜通过波长扫描进行叠层成像


近年来，叠层摄影已成为一种流行的显微镜计算成像方法。在目前的工作中，我们采用了由神经网络增强的波长扫描叠层成像技术，通过光纤内窥镜进行成像。使用单模光纤实现不同波长的物体照明，而多芯光纤则收集来自远处的衍射光。使用 U-Net 多层卷积神经网络，从近端记录的强度图案恢复多芯光纤远端的衍射图案。当恢复的衍射图案就位后，可以使用叠层摄影算法重建相位物体。物体重建的质量随着使用的波长数量的增加而提高。与端到端神经网络的比较凸显了这种两步混合系统的有效性和实用性。这种替代且简化的叠层描记内窥镜设置通过神经网络和波长扫描提供了显着的改进。