High-definition image transmission through dynamically perturbed multimode fiber by a self-attention based neural network.
Opt Lett
; 48(10): 2764-2767, 2023 May 15.
Article
em En
| MEDLINE
| ID: mdl-37186760
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.
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01-internacional
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MEDLINE
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article