@article {https://doi.org/10.1002/lpor.202000553,
	title = {Image Transmission Through a Dynamically Perturbed Multimode Fiber by Deep Learning},
	journal = {Laser \& Photonics Reviews},
	volume = {15},
	number = {10},
	year = {2021},
	pages = {2000553},
	abstract = {Abstract When multimode optical fibers are perturbed, the data that is transmitted through them is scrambled. This presents a major difficulty for many possible applications, such as multimode fiber based telecommunication and endoscopy. To overcome this challenge, a deep learning approach that generalizes over mechanical perturbations is presented. Using this approach, successful reconstruction of the input images from intensity-only measurements of speckle patterns at the output of a 1.5 m-long randomly perturbed multimode fiber is demonstrated. The model{\textquoteright}s success is explained by hidden correlations in the speckle of random fiber\&nbsp;conformations.},
	keywords = {deep learning, endoscopy, image reconstruction, Imaging, multimode optical fibers, Speckle},
	doi = {https://doi.org/10.1002/lpor.202000553},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/lpor.202000553},
	author = {Shachar Resisi and Popoff,Sebastien M. and Bromberg, Yaron}
}
