Abstract
We implement faithful multimode fiber (MMF) image transmission by a
self-attention-based neural network. Compared with a real-valued
artificial neural network (ANN) based on a convolutional neural
network (CNN), our method utilizes a self-attention mechanism to
achieve a higher image quality. The enhancement measure (EME) and
structural similarity (SSIM) of the dataset collected in the
experiment improved by 0.79 and 0.04; the total number of parameters
can be reduced by up to 25%. To enhance the robustness of the neural
network to MMF bending in image transmission, we use a simulation
dataset to prove that the hybrid training method is helpful in MMF
transmission of a high-definition image. Our findings may pave the way
for simpler and more robust single-MMF image transmission schemes with
hybrid training; SSIM on datasets under different disturbances improve
by 0.18. This system has the potential to be applied to various
high-demand image transmission tasks, such as endoscopy.
Funder
National Natural Science Foundation of
China
Subject
Atomic and Molecular Physics, and Optics
Cited by
6 articles.
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