Class-specific diffractive cameras based on deep learning-designed surfaces

Xuxi Zhou; Shuming Wang

doi:10.1038/s41377-022-00974-7

Go to Nature Website

Your Location：

Home >

Browse articles >

Class-specific diffractive cameras based on deep learning-designed surfaces

News & Views

- Class-specific diffractive cameras based on deep learning-designed surfaces
- Class-specific diffractive cameras based on deep learning-designed surfaces
- LSA 2022年11卷第10期页码：2262-2263
- Affiliations：
  
  National Laboratory of Solid-State Microstructures, School of Physics, Nanjing University, Nanjing 210093, China
- Author bio：
  
  Shuming Wang (wangshuming@nju.edu.cn)
- Funds：
- DOI：10.1038/s41377-022-00974-7
  中图分类号：
- 纸质出版日期：2022-10-31，
  
  网络出版日期：2022-09-29，
- Accepted：
Scan QR Code
Class-specific diffractive cameras based on deep learning-designed surfaces[J]. LSA, 2022,11(10):2262-2263.

Zhou, X. X. & Wang, S. M. Class-specific diffractive cameras based on deep learning-designed surfaces. Light: Science & Applications, 11, 2262-2263 (2022).
Class-specific diffractive cameras based on deep learning-designed surfaces[J]. LSA, 2022,11(10):2262-2263. DOI： 10.1038/s41377-022-00974-7.

Zhou, X. X. & Wang, S. M. Class-specific diffractive cameras based on deep learning-designed surfaces. Light: Science & Applications, 11, 2262-2263 (2022). DOI： 10.1038/s41377-022-00974-7.

摘要

Abstract

Recently

a new diffractive camera design based on transmissive surfaces structured using deep learning is proposed. It performs class-specific imaging of target objects and all-optical deletion of other classes of objects

which will promote the development of privacy-preserving digital cameras and mission-specific data.

关键词

Keywords

references

F. Dufaux, T. Ebrahimi. Scrambling for privacy protection in video surveillance systems. IEEE Trans. Circuits Syst. Video Technol., 2008. 181168-1174. DOI:10.1109/TCSVT.2008.928225http://doi.org/10.1109/TCSVT.2008.928225.

Pittaluga, F., Koppal, S. & Chakrabarti, A. Learning privacy preserving encodings through adversarial training. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) 791–799 (IEEE, 2019). https://doi.org/10.1109/WACV.2019.00089https://doi.org/10.1109/WACV.2019.00089.

Uittenbogaard, R. et al. Privacy protection in street-view panoramas using depth and multi-view imagery. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10573–10582 (IEEE, 2019). https://doi.org/10.1109/CVPR.2019.01083https://doi.org/10.1109/CVPR.2019.01083.

Chattopadhyay, A. & Boult, T. E. PrivacyCam: a privacy preserving camera using uCLinux on the Blackfn DSP. In 2007 IEEE Conference on Computer Vision and Pattern Recognition 1–8 (IEEE, 2007), https://doi.org/10.1109/CVPR.2007.383413https://doi.org/10.1109/CVPR.2007.383413.

53 Important Statistics About How Much Data Is Created Every Day. Financesonline. com https://financesonline.com/how-much-data-is-created-every-day/https://financesonline.com/how-much-data-is-created-every-day/ (2021).

Pittaluga, F. & Koppal, S. J. Privacy preserving optics for miniature vision sensors. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 314–324 (IEEE, 2015). https://doi.org/10.1109/CVPR.2015.7298628https://doi.org/10.1109/CVPR.2015.7298628.

X. Lin, 等. All-optical machine learning using diffractive deep neural networks. Science, 2018. 3611004-1008. DOI:10.1126/science.aat8084http://doi.org/10.1126/science.aat8084.

B. Bai, 等. To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects. eLight, 2022. 214DOI:10.1186/s43593-022-00021-3http://doi.org/10.1186/s43593-022-00021-3.

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据