Multimode fiber-based greyscale image projector enabled by neural networks with high generalization ability

PLUTO / PLUTO-2 Spatial Light Modulators
Deep Learning / Neuronal Network
Published on:
Authors: Wang, Jian; Zhong, Guangchao; Wu, Daixuan; Huang, Sitong; Luo, Zhi-Chao & Shen, Yuecheng
Abstract:

“Multimode fibers (MMFs) are emerging as promising transmission media for delivering images. However, strong mode coupling inherent in MMFs induces difficulties in directly projecting two-dimensional images through MMFs. By training two subnetworks named Actor-net and Model-net synergetically, [Nature Machine Intelligence 2, 403 (2020) [CrossRef] alleviated this issue and demonstrated projecting images through MMFs with high fidelity. In this work, we make a step further by improving the generalization ability to greyscale images. The modified projector network contains three subnetworks, namely forward-net, backward-net, and holography-net, accounting for forward propagation, backward propagation, and the phase-retrieval process. As a proof of concept, we experimentally trained the projector network using randomly generated phase maps and their corresponding resultant speckle images output from a 1-meter-long MMF. With the network being trained, we successfully demonstrated projecting binary images from MNIST and EMNIST and greyscale images from Fashion-MNIST, exhibiting averaged Pearson’s correlation coefficients of 0.91, 0.92, and 0.87, respectively. Since all these projected images have never been seen by the projector network before, a strong generalization ability in projecting greyscale images is confirmed.”

Open Access

Publication: Optics Express
Issue/Year: Optics Express, Volume 31; Number 3; Pages 4839; 2023
DOI: 10.1364/oe.482551
Link: https://doi.org/10.1364/OE.482551

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