The obstacle of imaging through multimode fibers (MMFs) is encountered due to the fact that the inherent mode dispersion and mode coupling lead the output of the MMF to be scattered and bring about image distortions. As a result, only noise-like speckle patterns can be formed on the distal end of the MMF. We propose a deep learning model exploited for computational imaging through an MMF, which contains an autoencoder (AE) for feature extraction and image reconstruction and self-normalizing neural networks (SNNs) sandwiched and employed for high-order feature representation. It was demonstrated both in simulations and in experiments that the proposed AE-SNN combined deep learning model could reconstruct image information from various binary amplitude-only targets going through a 5-meter-long MMF. Simulations indicate that our model works effectively even in the presence of system noise, and the experimental results prove that the method is valid for image reconstruction through the MMF. Enabled by the spatial variability and the self-normalizing properties, our model can be generalized to solve varieties of other computational imaging problems.

中文翻译：

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基于AE-SNN组合深度学习模型的MMF二进制仅幅值图像重建

由于固有的模式色散和模式耦合导致MMF的输出被散射并导致图像失真，因此遇到了通过多模光纤（MMF）成像的障碍。结果，只能在MMF的远端上形成类似噪声的斑点图案。我们提出了一种通过MMF用于计算成像的深度学习模型，该模型包含一个自动编码器（AE），用于特征提取和图像重建以及自归一化神经网络（SNN），将其夹在中间并用于高阶特征表示。在仿真和实验中都证明，提出的AE-SNN组合深度学习模型可以从经过5米长的MMF的各种仅二进制幅度目标中重建图像信息。仿真表明，该模型即使在存在系统噪声的情况下也能有效工作，实验结果证明该方法对于通过MMF进行图像重建是有效的。由于空间变异性和自归一化属性的支持，我们的模型可以推广为解决各种其他计算成像问题。