Dual-modality ghost diffraction in a complex disordered environment using untrained neural networks

Abstract

We report a dual-modality ghost diffraction (GD) system to simultaneously enable high-fidelity data transmission and high-resolution object reconstruction through complex disordered media using an untrained neural network (UNN) with only one set of realizations. The pixels of a 2D image to be transmitted are sequentially encoded into a series of random amplitude-only patterns using a UNN without labels and datasets. The series of random patterns generated is sequentially displayed to interact with an object placed in a designed optical system through complex disordered media. The realizations recorded at the receiving end are used to retrieve the transmitted data and reconstruct the object at the same time. The experimental results demonstrate that the proposed dual-modality GD system can robustly enable high-fidelity data transmission and high-resolution object reconstruction in a complex disordered environment. This could be a promising step toward the development of AI-driven compact optical systems with multiple modalities through complex disordered media.