Spectrally encoded machine vision using trainable materials

Jingxi Li; Deniz Mengu; Nezih T. Yardimci; Xurong Li; Yi Luo; Muhammed Veli; Yair Rivenson; Mona Jarrahi; Aydogan Ozcan

doi:10.1117/12.2580625

5 March 2021 Spectrally encoded machine vision using trainable materials

Jingxi Li, Deniz Mengu, Nezih T. Yardimci, Xurong Li, Yi Luo, Muhammed Veli, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan

Proceedings Volume 11703, AI and Optical Data Sciences II; 117031J (2021) https://doi.org/10.1117/12.2580625
Event: SPIE OPTO, 2021, Online Only

Abstract

Using deep learning-based training of diffractive layers we designed single-pixel machine vision systems to all-optically classify images by maximizing the output power of the wavelength corresponding to the correct data-class. We experimentally validated our diffractive designs using a plasmonic nanoantenna-based time-domain spectroscopy setup and 3D-printed diffractive layers to successfully classify the images of handwritten-digits using a single-pixel and snap-shot illumination. Furthermore, we trained a shallow electronic neural network as a decoder to reconstruct the images of the input objects, solely from the power detected at ten distinct wavelengths, also demonstrating the success of this platform as a task-specific, single-pixel imager.

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Jingxi Li, Deniz Mengu, Nezih T. Yardimci, Xurong Li, Yi Luo, Muhammed Veli, Yair Rivenson, Mona Jarrahi, and Aydogan Ozcan "Spectrally encoded machine vision using trainable materials", Proc. SPIE 11703, AI and Optical Data Sciences II, 117031J (5 March 2021); https://doi.org/10.1117/12.2580625

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