When multimode optical fibers are perturbed, the data that is transmitted through them is scrambled. This presents a major difficulty for many possible applications, such as multimode fiber based telecommunication and endoscopy. To overcome this challenge, a deep learning approach that generalizes over mechanical perturbations is presented. Using this approach, successful reconstruction of the input images from intensity-only measurements of speckle patterns at the output of a 1.5 m-long randomly perturbed multimode fiber is demonstrated. The model's success is explained by hidden correlations in the speckle of random fiber conformations.

中文翻译：

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通过深度学习通过动态扰动多模光纤传输图像

当多模光纤受到干扰时，通过它们传输的数据会被加扰。这为许多可能的应用带来了重大困难，例如基于多模光纤的电信和内窥镜检查。为了克服这一挑战，提出了一种泛化机械扰动的深度学习方法。使用这种方法，演示了从 1.5 m 长随机扰动多模光纤输出端的散斑图案的仅强度测量成功重建输入图像。该模型的成功是通过随机纤维构象散斑中隐藏的相关性来解释的。