基于可微光线追踪与机器学习的多模光管百万像素图像高效数据传输
Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning
AI总结：
针对多模光纤图像解码计算复杂度高的问题，提出结合可训练光线追踪数字孪生体与U-Net的方法，用基于冯·米塞斯分布的可微互信息估计器端到端训练，实现66 kB数据高效恢复，性能优于本征模方案。
摘要：
由于多模光纤能在紧凑系统中高效限制并传输光，通过多模光纤恢复传输图像的研究正受到越来越多的关注。本文展示了基于机器学习的大规模数字图像（页面）解码方法，旨在最大化光存储应用的页面容量。研究采用毫米级方形截面波导，对8位空间光调制器成像，将数据以符号矩阵形式呈现。通常情况下，解码器要解码空间加扰数据中的n个符号，会面临难以承受的O(n²)计算复杂度。但通过将实验装置的数字孪生体与U-Net结合，仅使用高效的卷积运算即可恢复最高66 kB的数据。本文对比了基于可训练光线追踪的数字孪生体与基于本征模的数字孪生体，结果表明前者性能更优，因为它能通过适配光学缺陷来克服仿真与实验之间的差距。研究使用基于冯·米塞斯分布的可微互信息估计器对整个流程进行端到端训练，该估计器普遍适用于相位编码信道。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
