Image Reconstruction Using Pre-Trained Autoencoder on Multimode Fiber Imaging System
Image Reconstruction Using Pre-Trained Autoencoder on Multimode Fiber Imaging System
复制标题
在多模光纤成像系统上使用预训练自动编码器进行图像重建
DOI：
10.1109/lpt.2020.2992819
复制
发表时间：
2020-07-01
影响因子：
2.6
通讯作者：
Xu, Kun
中科院分区：
文献类型：
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作者：
Li, Yuang;Yu, Zhenming;Xu, Kun
基于多模光纤(MMF)的内窥镜可以达到高分辨率，并且足够精细地用于活体成像。然而，由于MMF的模式串扰和对环境的敏感性，接收到的图像存在斑点，这使得图像重建成为主要挑战。我们提出使用预先训练的自动编码器来从斑点噪声重建到原始图像，该方法具有高性能和快速的收敛速度。该网络体系结构包括编码器和解码器两部分。在第一步中，我们预先训练网络以初始化解码器的参数。在第二步，网络学习斑点图像与原始图像之间的映射关系。为了验证该方法的有效性，我们用50-MU-TEXT{m}-CORE进行了一米以上的MMF传输实验。通过结构相似度(SSIM)、峰值信噪比(PSNR)和均方误差(MSE)来评价算法的性能。与U-Net相比，使用预先训练的自动编码器后，SSIM提高了11%。此外，预先训练的自动编码器的训练既快又稳定。
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.