Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wavefront propagates along its length is highly sensitive to wavelength changes. We use a tunable laser to implement a wavelength drift with a controlled bandwidth, aiming to estimate the DNN’s performance in different cases and identify the limitations. We find that when the DNNs are trained with a dataset which includes the noise induced by wavelength changes, successful classification of a speckle pattern can be performed even for a large wavelength bandwidth drift. A single training step is found to be sufficient for high classification accuracy, removing the need for time-consuming recalibration at each wavelength.

中文翻译：

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在存在波长漂移的情况下通过多模光纤进行基于深度学习的图像分类

在存在波长漂移的情况下，深度神经网络 (DNN) 用于在信息通过多模光纤 (MMF) 传播后恢复信息。当输入波前沿其长度传播时，在 MMF 输出端生成的散斑图案的强度分布对波长变化高度敏感。我们使用可调谐激光器来实现带宽受控的波长漂移，旨在估计 DNN 在不同情况下的性能并确定其局限性。我们发现，当 DNN 使用包含波长变化引起的噪声的数据集进行训练时，即使对于大的波长带宽漂移，也可以成功地对散斑图案进行分类。发现单个训练步骤足以获得高分类精度，