论文标题
深度学习通过灵活的多模纤维传输图像
Image transmission through a flexible multimode fiber by deep learning
论文作者
论文摘要
当多模光纤受到干扰时，通过它们传输的数据会扰乱。对于许多可能的应用，例如基于多模纤维的电信和内窥镜检查，这是一个主要的困难。为了克服这一挑战，提出了一种对机械扰动的概括的深度学习方法。使用这种方法，证明了从强度的仅对斑点图案的仅在1.5米长的随机扰动的多模纤维的输出中成功重建输入图像。该模型的成功是通过随机纤维构象斑点中的隐藏相关性来解释的。
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 meter-long randomly perturbed multimode fiber is demonstrated. The model's success is explained by hidden correlations in the speckle of random fiber conformations.
