Improving the signal-to-noise ratio of single-pixel imaging using digital microscanning

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Sun, Mingjie ; Edgar, Matthew P. ; Phillips, David B. ; Gibson, Graham M. ; Padgett, Miles J. (2016)
  • Publisher: OSA Publishing
  • Related identifiers: doi: 10.1364/OE.24.010476
  • Subject:
    arxiv: Computer Science::Computer Vision and Pattern Recognition

Single-pixel cameras provide a means to perform imaging at wavelengths where pixelated detector arrays are expensive or limited. The image is reconstructed from measurements of the correlation between the scene and a series of masks. Although there has been much research in the field in recent years, the fact that the signal-to-noise ratio (SNR) scales poorly with increasing resolution has been one of the main limitations prohibiting the uptake of such systems. Microscanning is a technique that provides a final higher resolution image by combining multiple images of a lower resolution. Each of these low resolution images is subject to a sub-pixel sized lateral displacement. In this work we apply a digital microscanning approach to an infrared single-pixel camera. Our approach requires no additional hardware, but is achieved simply by using a modified set of masks. Compared to the conventional Hadamard based single-pixel imaging scheme, our proposed framework improves the SNR of reconstructed images by ∼ 50 % for the same acquisition time. In addition, this strategy also provides access to a stream of low-resolution ‘preview’ images throughout each high-resolution acquisition.
  • References (22)
    22 references, page 1 of 3

    9. J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78, 061802 (2008).

    10. A. C. Sankaranarayanan, C. Studer, and R. G. Baraniuk, “Cs-muvi: Video compressive sensing for spatialmultiplexing cameras,” in Proceedings of IEEE International Conference on Computational Photography (IEEE 2012), pp. 1-10 .

    11. N. Radwell, K. J. Mitchell, G. M. Gibson, M. P. Edgar, R. W. Bowman, and M. J. Padgett, “Single-pixel infrared and visible microscope,” Optica 1, 285-289 (2014).

    12. M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5, 10669 (2015).

    13. F. Ferri, D. Magatii,, L. A. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. A 104, 253603 (2010).

    14. B. Sun, S. S. Welsh, M. P. Edgar, J. H. Shapiro, and M. J. Padgett, “Normalized ghost imaging,” Opt. Express 20, 16892-16901 (2012).

    15. K.-H. Luo, B.-Q. Huang, W.-M. Zheng, and L.-A. Wu, “Nonlocal imaging by conditional averaging of random reference measurements,” Chin. Phys. Lett. 29, 074216 (2012).

    16. B. Sun, M. P. Edgar, R. W. Bowman, L. E. Vittert, S. S. Welsh, A. Bowman, and M. J. Padgett, “Differential computational ghost imaging,” in Computational Optical Sensing and Imaging Conference (Optical Society of America, 2013), paper CTu1C-4.

    17. M.-J. Sun, M.-F. Li, and L.-A. Wu, “Nonlocal imaging of a reflective object using positive and negative correlations,” Appl. Opt. 54, 7494-7499 (2015).

    18. S.-C. Song, M.-J. Sun, and L.-A. Wu, “Improving the signal-to-noise ratio of thermal ghost imaging based on positive-negative intensity correlation,” Opt. Commun. 366, 8-12 (2016).

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