Computational imaging without a computer: seeing through random diffusers at the speed of light

Yi Luo; Yifan Zhao; Jingxi Li; Ege Çetintaş; Yair Rivenson; Mona Jarrahi; Aydogan Ozcan

doi:10.1186/s43593-022-00012-4

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Computational imaging without a computer: seeing through random diffusers at the speed of light

光：快讯（英文） 2022年2卷第1期

Research Article | Updated：2024-09-28

- Computational imaging without a computer: seeing through random diffusers at the speed of light
- Computational imaging without a computer: seeing through random diffusers at the speed of light
- Affiliations：
  
  Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
  Bioengineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
  California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Author bio：
  
  *ozcan@ucla.edu
- Funds：
  
  the U.S. National Science Foundation and Fujikura
- DOI：10.1186/s43593-022-00012-4
  中图分类号：
- 纸质出版日期：2022-12，
  
  网络出版日期：2022-01-26，
  
  收稿日期：2021-12-03，
  
  修回日期：2021-12-31，
  
  录用日期：2022-01-08
- Accepted：
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Computational imaging without a computer: seeing through random diffusers at the speed of light[J]. 光：快讯（英文）, 2022,2(1).

Yi Luo, Yifan Zhao, Jingxi Li, et al. Computational imaging without a computer: seeing through random diffusers at the speed of light[J]. eLight, 2022,2(1).
Computational imaging without a computer: seeing through random diffusers at the speed of light[J]. 光：快讯（英文）, 2022,2(1). DOI： 10.1186/s43593-022-00012-4.

Yi Luo, Yifan Zhao, Jingxi Li, et al. Computational imaging without a computer: seeing through random diffusers at the speed of light[J]. eLight, 2022,2(1). DOI： 10.1186/s43593-022-00012-4.

摘要

Abstract

Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers. Here

we present a computer-free

all-optical image reconstruction method to see through random diffusers at the speed of light. Using deep learning

a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown

random phase diffusers. After the training stage

which is a one-time effort

the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown

new phase diffuser. We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown

random diffusers

never used during training. Unlike digital methods

all-optical diffractive reconstructions do not require power except for the illumination light. This diffractive solution to see through diffusers can be extended to other wavelengths

and might fuel various applications in biomedical imaging

astronomy

atmospheric sciences

oceanography

security

robotics

autonomous vehicles

among many others.

关键词

Keywords

Imaging through diffusersComputational imagingDiffractive neural networkDeep learning

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