Abstract
We present a machine-learning-based method for light focusing through scattering media. In this method, the optical process in a scattering medium is computationally inverted based on a nonlinear regression algorithm with a number of training input–output pairs through the medium, and an input optimized for a target output is calculated. We experimentally demonstrate focusing via a process involving randomness due to a scattering medium and nonlinearity due to double modulation with a spatial light modulator. Our approach realizes model-free control of optical fields, where optical processes or models are unknown.
© 2017 Optical Society of America
Full Article |
PDF Article
More Like This
References
You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription
Tables (1)
You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription
Equations (10)
You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription
Metrics
You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access OSA Member Subscription