Optical Computing For Object Classification Through Diffusive Random Media

Researchers at UCLA have recently developed an all-optical method that enables objects to be classified through unknown random diffusers using diffractive deep neural networks (DNNs). DNNs form a free-space optical computing platform that has attracted growing research interest in recent years. DNNs compute a given task by modulating the light diffraction through a series of spatially structured surfaces, collectively forming an all-optical computer that can operate at the speed of light. This method has potential applications in biomedical imaging, oceanography, security, robotics, and autonomous driving.