Diffractive decoders project super-resolved images

Cagatay Isil; Deniz Mengu; Yifan Zhao; Anika Tabassum; Jingxi Li; Yi Luo; Mona Jarrahi; Aydogan Ozcan

doi:10.1117/12.2648509

17 March 2023 Diffractive decoders project super-resolved images

Cagatay Isil, Deniz Mengu, Yifan Zhao, Anika Tabassum, Jingxi Li, Yi Luo, Mona Jarrahi, Aydogan Ozcan

Proceedings Volume PC12438, AI and Optical Data Sciences IV; PC1243801 (2023) https://doi.org/10.1117/12.2648509
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

We report an electronic encoder (formed by a convolutional neural network) and a diffractive decoder (formed by spatially-structured diffractive layers) that are jointly optimized using deep learning to project super-resolved images at the output plane using a low-resolution spatial-light modulator (SLM). This diffractive super-resolution display performs ~4x optical super-resolution, corresponding to a ~16x increase in the space-bandwidth product. This diffractive display was experimentally demonstrated using 3D-printed diffractive decoders operating at the THz spectrum. Diffractive super-resolution image displays can be used to build compact, low-power, and computationally efficient HR projectors operating at visible wavelengths and other parts of the electromagnetic spectrum.

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Citation Download Citation

Cagatay Isil, Deniz Mengu, Yifan Zhao, Anika Tabassum, Jingxi Li, Yi Luo, Mona Jarrahi, and Aydogan Ozcan "Diffractive decoders project super-resolved images", Proc. SPIE PC12438, AI and Optical Data Sciences IV, PC1243801 (17 March 2023); https://doi.org/10.1117/12.2648509

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