Deep learning-based image transmission through a multi-mode fiber

Sahin Kurekci; A. Tarık Temur; M. Ekrem Odabas; Gilda Afshari; Emre Yüce

doi:10.1117/12.2554944

9 April 2020 Deep learning-based image transmission through a multi-mode fiber

Sahin Kurekci, A. Tarık Temur, M. Ekrem Odabas, Gilda Afshari, Emre Yüce

Proceedings Volume 11351, Unconventional Optical Imaging II; 1135126 (2020) https://doi.org/10.1117/12.2554944
Event: SPIE Photonics Europe, 2020, Online Only

Abstract

Image transmission through a multi-mode fiber is a difficult task given the complex interference of light through the fiber that leads to random speckle patterns at the distal end of the fiber. With traditional methods and techniques, it is impractical to reconstruct a high-resolution input image by using the information obtained from the intensity of the corresponding output speckle alone. In this work, we train three Convolutional Neural Networks (CNNs) with input-output couples of a multi-mode fiber and test the learning with images outside the learning set. The three implemented deep learning models have modern UNet, ResNet and VGGNet architectures and are trained with 31,200 grey-scale handwritten letters of the Latin alphabet. After the training, 5,200 images outside the learning set are used for testing and it was shown that the models successfully reconstruct the input images from the output random speckle patterns with average fidelities ranging from 81% to 90%. Our results show the superiority of the ResNet based architecture over UNet and VGGNet in reconstruction accuracy, achieving up to 97% fidelity in a short amount of time. This can be attributed to the success of the ResNet architecture in learning non-linear systems compared to its counterparts. We believe that the implementation of machine learning techniques to imaging, along with its contributions to biophysics, can reshape the telecommunication industry and thus will be a cornerstone in future optics and photonics studies.

Conference Presentation

Citation Download Citation

Sahin Kurekci, A. Tarık Temur, M. Ekrem Odabas, Gilda Afshari, and Emre Yüce "Deep learning-based image transmission through a multi-mode fiber", Proc. SPIE 11351, Unconventional Optical Imaging II, 1135126 (9 April 2020); https://doi.org/10.1117/12.2554944

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available