Multimode fiber (MMF) based endoscopy could reach high resolution and is fine enough for vivo imaging. However, the received images are speckles due to the mode crosstalk and sensitivity to environment of MMF, which makes image reconstruction the main challenge. We propose to use pre-trained autoencoder for image reconstruction from speckles to original images, which shows high performance and fast convergence speed. The network architecture includes two parts, i.e., encoder and decoder. In the first step, we pre-train the network to initialize the parameters of the decoder. In the second step, the network can learn the mapping relation between speckle patterns and original images. We conduct experiment of transmitting over one-meter MMF with 50- $\mu \text{m}$ -core to verify this method. Structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) are measured to evaluate the performance. Compared with U-net, the SSIM increases by 11% with pre-trained autoencoder. Moreover, the training of pre-trained autoencoder is both fast and steady.

中文翻译：

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在多模光纤成像系统上使用预训练的自动编码器进行图像重建

基于多模光纤 (MMF) 的内窥镜可以达到高分辨率，并且对于体内成像来说足够精细。然而，由于模式串扰和MMF对环境的敏感性，接收到的图像是散斑，这使得图像重建成为主要挑战。我们建议使用预训练的自动编码器进行从散斑到原始图像的图像重建，具有高性能和快速收敛速度。网络架构包括两部分，即编码器和解码器。第一步，我们预训练网络以初始化解码器的参数。第二步，网络可以学习散斑图案和原始图像之间的映射关系。我们使用 50- $\mu \text{m}$ -core 进行了超过一米 MMF 传输的实验来验证这种方法。结构相似性（SSIM），测量峰值信噪比 (PSNR) 和均方误差 (MSE) 以评估性能。与 U-net 相比，使用预训练的自动编码器，SSIM 增加了 11%。此外，预训练自编码器的训练既快速又稳定。