All-optical image classification through unknown random diffusers using a single-pixel diffractive network

Bijie Bai; Yuhang Li; Yi Luo; Xurong Li; Ege Çetintaş; Mona Jarrahi; Aydogan Ozcan

doi:10.1038/s41377-023-01116-3

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All-optical image classification through unknown random diffusers using a single-pixel diffractive network

Original Articles

- All-optical image classification through unknown random diffusers using a single-pixel diffractive network
- Light: Science & Applications Vol. 12, Issue 4, Pages: 570-584(2023)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA
  2.Bioengineering Department, University of California, Los Angeles, California 90095, USA
  3.California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-023-01116-3
  CLC：
- Published：30 April 2023，
  
  Published Online：9 March 2023，
  
  Received：20 August 2022，
  
  Revised：22 February 2023，
  
  Accepted：22 February 2023
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Bai, B. J.et al. All-optical image classification through unknown random diffusers using a single-pixel diffractive network.Light: Science & Applications,12, 570-584 (2023).
DOI：

Bai, B. J.et al. All-optical image classification through unknown random diffusers using a single-pixel diffractive network.Light: Science & Applications,12, 570-584 (2023). DOI： 10.1038/s41377-023-01116-3.

摘要

Abstract

Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields. Recent deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image sensor. These methods demand relatively large-scale computing using deep neural networks running on digital computers. Here

we present an all-optical processor to directly classify unknown objects through unknown

random phase diffusers using broadband illumination detected with a single pixel. A set of transmissive diffractive layers

optimized using deep learning

forms a physical network that all-optically maps the spatial information of an input object behind a random diffuser into the power spectrum of the output light detected through a single pixel at the output plane of the diffractive network. We numerically demonstrated the accuracy of this framework using broadband radiation to classify unknown handwritten digits through random new diffusers

never used during the training phase

and achieved a blind testing accuracy of 87.74 ± 1.12%. We also experimentally validated our single-pixel broadband diffractive network by classifying handwritten digits "0" and "1" through a random diffuser using terahertz waves and a 3D-printed diffractive network. This single-pixel all-optical object classification system through random diffusers is based on passive diffractive layers that process broadband input light and can operate at any part of the electromagnetic spectrum by simply scaling the diffractive features proportional to the wavelength range of interest. These results have various potential applications in

e.g.

biomedical imaging

security

robotics

and autonomous driving.

关键词

Keywords

references

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