We report an optical diffractive decoder with an electronic encoder network to facilitate the accurate transmission of optical information of interest through unknown random phase diffusers along the optical path. This hybrid electronic-optical model was trained via supervised learning, and comprises a convolutional neural network-based encoder and jointly-trained passive diffractive layers. After their joint-training using deep learning, our hybrid model can accurately transfer optical information even in the presence of unknown phase diffusers, generalizing to new random diffusers never seen before. We experimentally validated this framework using a 3D-printed diffractive network, axially spanning <70λ, where λ=0.75mm is the illumination wavelength.
|