Learning-based signal retrieval from scattering media

Jun Tanida; Ryoichi Horisaki

doi:10.1117/12.2322800

7 September 2018 Learning-based signal retrieval from scattering media

Jun Tanida, Ryoichi Horisaki

Proceedings Volume 10834, Speckle 2018: VII International Conference on Speckle Metrology; 108340O (2018) https://doi.org/10.1117/12.2322800
Event: SPECKLE 2018: VII International Conference on Speckle Metrology, 2018, Janów Podlaski, Poland

Abstract

Machine learning is an efficient tool to estimate input signals from the output ones for nonlinear systems. In case of object observation through scattering media with coherent illumination, the output signals could be speckle patterns highly disordered from the input ones. Even for the case, machine learning is effective to retrieve the input information. In the method, a number of pairs of input and output signals are used for training, and then untrained input signals can be retrieved from the observed output signals. We demonstrated object recognition, imaging, and focusing through scattering media and confirmed effectiveness of the presented method.

Citation Download Citation

Jun Tanida and Ryoichi Horisaki "Learning-based signal retrieval from scattering media", Proc. SPIE 10834, Speckle 2018: VII International Conference on Speckle Metrology, 108340O (7 September 2018); https://doi.org/10.1117/12.2322800

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