基于深度学习的多模光纤散射介质成像重建

Image Reconstruction of Multimode Fiber Scattering Media Based on Deep Learning

多模光纤是一种厚散射介质,当目标图像经过多模光纤传输时将形成多种模式耦合,从而在光纤的输出端生成散斑图案。基于深度学习对多模光纤成像进行复原,解决了厚散射介质成像失真的问题。采用DenseUnet,并以散斑图样作为模型的输入来重建目标图像。DenseUnet模型采用融合机制加深了网络的深度,提高了重建的准确性,并具有很好的鲁棒性。实验结果表明,DenseUnet可以很好地对具有不同长度的多模光纤产生的散斑图像进行重建。

Multimode fiber is a thick scattering medium. When the target image is projected onto the multimode optical fiber, multimode coupling will occur, thereby generating speckle images at the output of the fiber. In this work, multimode optical fiber imaging is restored based on deep learning, and the distortion of thick scattering media imaging is solved. DenseUnet is used and the speckle image is used as the input of the model for reconstructing the target image. The DenseUnet model employs a fusion mechanism to deepen the network depth, thus, improving the reconstruction accuracy and realizing good robustness. The experimental results reveal that DenseUnet can be used to reconstruct speckle images produced by multimode optical fibers with different lengths.