Artificial neural networks for controlling light delivery through complex media

Imaging and delivering of light in a controlled manner through complex media such as glass diffusers, biological tissue, or multimode optical fibers, is limited by the scattering of light when it propagates through the material. Different methods based on spatial light modulators can be used to prior shaping the light beam to compensate for the scattering, this including phase conjugation, hall-climbing algorithms, or the so-called transmission matrix approach. Here, we develop a machine-learning approach for light delivery through complex media. Using pairs of binary intensity patterns and intensity measurements we train artificial neural networks (ANNs) to provide the wavefront corrections necessary to shape the beam after the scatterer. Additionally, we show that ANNs pave the way towards finding a functional relationship between reflected and transmitted light through the scatterer that can be used for light delivery in transmission by using reflected light. We expect that our approach showing the versatility of ANNs for light shaping will open new doors towards efficiently and flexibly correcting for scattering, in particular by only using reflected light