Light scattering control in transmission and reflection with neural networks

Abstract: Scattering often limits the controlled delivery of light in applications such as biomedical imaging, optogenetics, optical trapping, and fiber-optic communication or imaging. Such scattering can be controlled by appropriately shaping the light wavefront entering the material. Here, we demonstrate a machine-learning approach for light control. Using pairs of binary intensity patterns and intensity measurements we train neural networks (NNs) to provide the wavefront corrections necessary to shape the beam after the scatterer. Additionally, we demonstrate that NNs can be used to find a functional relationship between transmitted and reflected speckle patterns. As a proof of the validity of this relationship, we demonstrate focusing and scanning of light in transmission through opaque media using reflected light. Our approach demonstrates the versatility of NNs for light shaping and for efficiently and flexibly correcting for scattering. In particular, the feasibility of transmission control based on reflected light opens up new opportunities for applications in optical imaging, sensing, and light delivery.