2017-12-15 Welcome guest,  Sign In  |  Sign Up
Chin. Opt. Lett.
 Home  List of Issues    Issue 12 , Vol. 15 , 2017    10.3788/COL201715.121101

Double-threshold technique for achieving denoising in compressive imaging applications
Chao Wang1;2;3, Xuri Yao3, and Qing Zhao1
1 Center for Quantum Technology Research, School of Physics, [Beijing Institute of Technology], Beijing 1 00081 , China
2 [China Academy of Engineering Physics], Mianyang 62 1900, China
3 Key Laboratory of Electronics and Information Technology for Space Systems, [National Space Science Center], Chinese Academy of Sciences, Beijing 100190, China

Chin. Opt. Lett., 2017, 15(12): pp.121101

Topic:Imaging systems
Keywords(OCIS Code): 110.0110  110.2990  100.3010  

Single-pixel cameras, which employ either structured illumination or image modulation and compressive sensing algorithms, provide an alternative approach to imaging in scenarios where the use of a detector array is restricted or difficult because of cost or technological constraints. In this work, we present a robust imaging method based on compressive imaging that sets two thresholds to select the measurement data for image reconstruction. The experimental and numerical simulation results show that the proposed double-threshold compressive imaging protocol provides better image quality than previous compressive imaging schemes. Faster imaging speeds can be attained using this scheme because it requires less data storage space and computing time. Thus, this denoising method offers a very effective approach to promote the implementation of compressive imaging in real-time practical applications.

Copyright: © 2003-2012 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 View PDF (378 KB)


Posted online:2017/11/3

Get Citation: Chao Wang, Xuri Yao, and Qing Zhao, "Double-threshold technique for achieving denoising in compressive imaging applications," Chin. Opt. Lett. 15(12), 121101(2017)

Note: This work was supported by the National Natural Science Foundation of China (Nos. 11675014, 61605218, 61601442, 61575207, and 61474123), the National Major Scientific Instruments Development Project of China (No. 2013YQ030595), the National Defense Science and Technology Innovation Foundation of Chinese Academy of Sciences, the Science and Technology Innovation Foundation of Chinese Academy of Sciences (No. CXJJ-16S047), the Program of International Science and Technology Cooperation (No. 2016YFE0131500), and the Advance Research Project (No. 30102070101).


1. M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, IEEE Signal Process 25, 83 (2008).

2. D. Donoho, IEEE Trans. Inform. Theory 52, 1289 (2006).

3. E. Candes, and M. Wakin, IEEE Signal Process 25, 21 (2008).

4. N. Radwell, K. J. Mitchell, G. M. Gibson, M. P. Edgar, R. Bowman, and M. J. Padgett, Optica 1, 285 (2014).

5. M. P. Edgar, Sci. Rep. 5, 10669 (2015).

6. G. A. Howland, P. B. Dixon, and J. C. Howell, Appl. Opt. 50, 5917 (2011).

7. G. A. Howland, D. J. Lum, M. R. Ware, and J. C. Howell, Opt. Express 21, 23822 (2013).

8. M. J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamband, and M. J. Padgett, Nat. Commun. 7, 12010 (2016).

9. W. L. Gong, C. Q. Zhao, H. Yu, M. L. Chen, W. D. Xu, and S. S. Han, Sci. Rep. 6, 26133 (2016).

10. V. Studer, J. Bobin, M. Chahid, H. Moussavi, E. Candes, and M. Dahan, Proc. Natl. Acad. Sci. 109, E1679 (2012).

11. J. J. Field, D. G. Winters, and R. A. Bartels, Optica 3, 971 (2016).

12. Y. August, C. Vachman, Y. Rivenson, and A. Stern, Appl. Opt. 52, D46 (2013).

13. R. M. Lan, X. F. Liu, X. R. Yao, W. K. Yu, and G. J. Zhai, Opt. Commun. 366, 349 (2016).

14. W. L. Gong, and S. S. Han, Phys. Lett. A 376, 1519 (2012).

15. J. Chen, W. L. Gong, and S. S. Han, Phys. Lett. A 377, 1844 (2013).

16. X. R. Yao, L. Z. Li, X. F. Liu, W. K. Yu, and G. J. Zhai, Chin. Phys. B 24, 044203 (2015).

17. W. L. Gong, and S. S. Han, Sci. Rep. 5, 9280 (2015).

18. Y. Li, Q. Li, J. Hu, and Y. Zhao, Chin. Opt. Lett. 13, S11101 (2015).

19. B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, in Computational Optical Sensing and Imaging Conference (2013), paper?CTu1C-4.

20. W. K. Yu, X. F. Liu, X. R. Yao, C. Wang, Y. Zhai, and G. J. Zhai, Sci. Rep. 4, 5834 (2014).

21. M. J. Sun, M. P. Edgar, D. P. Phillips, G. M. Gibson, and M. J. Padgett, Opt. Express 24, 10476 (2016).

22. L. Wang, and S. M. Zhao, Photon. Res. 4, 240 (2016).

23. Z. Zhang, X. Ma, and J. Zhong, Nat. Commun. 6, 6225 (2015).

24. J. H. Shapiro, Phys. Rev. A 78, 061802 (2008).

25. X. Xu, E. Li, X. Shen, and S. Han, Chin. Opt. Lett. 13, 071101 (2015).

26. Q. Li, Z. Duan, H. Lin, S. Gao, S. Sun, and W. Liu, Chin. Opt. Lett. 14, 111103 (2016).

27. K. H. Luo, B. Q. Huang, W. M. Zheng, and L. A. Wu, Chin. Phys. Lett. 29, 074216 (2012).

28. M. F. Li, Y. R. Zhang, H. Fan, L. A. Wu, X. F. Liu, X. R. Yao, and K. H. Luo, Appl. Phys. Lett. 103, 211119 (2013).

29. M. F. Li, Y. R. Zhang, K. H. Luo, L. A. Wu, and H. Fan, Phys. Rev. A 87, 033813 (2013).

30. X. Yao, X. Liu, W. Yu, and G. Zhai, Chin. Opt. Lett. 13, 010301 (2015).

31. C. B. Xue, X. R. Yao, X. F. Liu, G. J. Zhai, Q. Zhao, and X. Y. Guo, Opt. Commun. 393, 118 (2017).

32. C. B. Li, “An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing,” Master thesis (Rice University, 2010).

Save this article's abstract as
Copyright©2014 Chinese Optics Letters 沪ICP备05015387