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Free-space optical information transfer through diffusive media is criticalin many applications, such as biomedical devices and optical communication, butremains challenging due to random, unknown perturbations in the optical path.In this work, we demonstrate an optical diffractive decoder with electronicencoding to accurately transfer the optical information of interest,corresponding to, e.g., any arbitrary input object or message, through unknownrandom phase diffusers along the optical path. This hybrid electronic-opticalmodel, trained using supervised learning, comprises a convolutional neuralnetwork (CNN) based electronic encoder and successive passive diffractivelayers that are jointly optimized. After their joint training using deeplearning, our hybrid model can transfer optical information through unknownphase diffusers, demonstrating generalization to new random diffusers neverseen before. The resulting electronic-encoder and the optical-decoder modelwere experimentally validated using a 3D-printed diffractive network thataxially spans less than 70 x lambda, where lambda = 0.75 mm is the illuminationwavelength in the terahertz spectrum, carrying the desired optical informationthrough random unknown diffusers. The presented framework can be physicallyscaled to operate at different parts of the electromagnetic spectrum, withoutretraining its components, and would offer low-power and compact solutions foroptical information transfer in free space through unknown random diffusivemedia.

Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding