Spatially incoherent diffractive optical processors for complex-valued universal linear transformations and image encryption

Xilin Yang; Md Sadman Sakib Rahman; Bijie Bai; Jingxi Li; Aydogan Ozcan

doi:10.1117/12.3027848

4 October 2024 Spatially incoherent diffractive optical processors for complex-valued universal linear transformations and image encryption

Xilin Yang, Md Sadman Sakib Rahman, Bijie Bai, Jingxi Li, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC1311812 (2024) https://doi.org/10.1117/12.3027848
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

We present a method for accurately performing complex-valued linear transformations with a Diffractive Deep Neural Network (D2NN) under spatially incoherent illumination. By employing 'mosaicing' and 'demosaicing' techniques, complex data are encoded into optical intensity patterns for all-optical diffractive processing, and then decoded back into the complex domain at the output aperture. This framework not only enhances the capabilities of D2NNs for visual computing tasks but also opens up new avenues for applications in image encryption under natural light conditions to demonstrate the potential of diffractive optical networks in modern visual information processing needs.

Conference Presentation

Citation Download Citation

Xilin Yang, Md Sadman Sakib Rahman, Bijie Bai, Jingxi Li, and Aydogan Ozcan "Spatially incoherent diffractive optical processors for complex-valued universal linear transformations and image encryption", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC1311812 (4 October 2024); https://doi.org/10.1117/12.3027848

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