Retrieving images transmitted through multi-mode fibers is of growing interest, thanks to their ability to confine and transport light efficiently in a compact system. Here, we demonstrate machine-learning-based decoding of large-scale digital images (pages), maximizing page capacity for optical storage applications. Using a millimeter-sized square cross-section waveguide, we image an 8-bit spatial light modulator, presenting data as a matrix of symbols. Normally, decoders will incur a prohibitive O(n^2) computational scaling to decode n symbols in spatially scrambled data. However, by combining a digital twin of the setup with a U-Net, we can retrieve up to 66 kB using efficient convolutional operations only. We compare trainable ray-tracing-based with eigenmode-based twins and show the former to be superior thanks to its ability to overcome the simulation-to-experiment gap by adjusting to optical imperfections. We train the pipeline end-to-end using a differentiable mutual-information estimator based on the von-Mises distribution, generally applicable to phase-coding channels.

翻译：通过多模光纤传输图像的检索技术因其紧凑系统中高效的光约束与传输能力而日益受到关注。本文面向光存储应用的最大页面容量需求，演示了基于机器学习的大规模数字图像解码方法。我们采用毫米级方形截面波导，对8位空间光调制器成像，将数据编码为符号矩阵。传统解码器在处理空间混乱数据中的n个符号时，会面临O(n^2)的计算复杂度瓶颈。通过将数字孪生系统与U-Net网络结合，我们仅使用高效卷积运算即可检索高达66kB的数据。实验对比了基于可训练光线追踪与特征模态的孪生系统，结果表明前者因能通过自适应光学缺陷克服仿真-实验差异而更具优势。基于冯·米塞斯分布的可微分互信息估计器实现了全流水线端到端训练，该方法可普适应用于相位编码信道。