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7118428 
Journal Article 
Binary amplitude-only image reconstruction through a MMF based on an AE-SNN combined deep learning model 
Chen, Hui; He, Z; Zhang, Z; Geng, Yi; Yu, W; , 
2020 
1 
Optics Express
ISSN: 1094-4087 
OPTICAL SOC AMER 
WASHINGTON 
30048-30062 
33114890 
WOS:000581074800106 
The obstacle of imaging through multimode fibers (IVLMFs) is encountered due to the fact that the inherent mode dispersion and mode coupling lead the output of the MMF to be scattered and bring about image distortions. As a result, only noise-like speckle patterns can be formed on the distal end of the MMF. We propose a deep learning model exploited for computational imaging through an MMF, which contains an autoencoder (AE) for feature extraction and image reconstruction and self-normalizing neural networks (SNNs) sandwiched and employed for high-order feature representation. It was demonstrated both in simulations and in experiments that the proposed AE-SNN combined deep learning model could reconstruct image information from various binary amplitude-only targets going through a 5-meter-long MMF. Simulations indicate that our model works effectively even in the presence of system noise, and the experimental results prove that the method is valid for image reconstruction through the MMF. Enabled by the spatial variability and the self-normalizing properties, our model can be generalized to solve varieties of other computational imaging problems. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement