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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 22, Iss. 6 — Mar. 24, 2014
  • pp: 7133–7144

Adaptive compressive ghost imaging based on wavelet trees and sparse representation

Wen-Kai Yu, Ming-Fei Li, Xu-Ri Yao, Xue-Feng Liu, Ling-An Wu, and Guang-Jie Zhai  »View Author Affiliations

Optics Express, Vol. 22, Issue 6, pp. 7133-7144 (2014)

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Compressed sensing is a theory which can reconstruct an image almost perfectly with only a few measurements by finding its sparsest representation. However, the computation time consumed for large images may be a few hours or more. In this work, we both theoretically and experimentally demonstrate a method that combines the advantages of both adaptive computational ghost imaging and compressed sensing, which we call adaptive compressive ghost imaging, whereby both the reconstruction time and measurements required for any image size can be significantly reduced. The technique can be used to improve the performance of all computational ghost imaging protocols, especially when measuring ultra-weak or noisy signals, and can be extended to imaging applications at any wavelength.

© 2014 Optical Society of America

OCIS Codes
(110.2990) Imaging systems : Image formation theory
(200.4740) Optics in computing : Optical processing
(110.1085) Imaging systems : Adaptive imaging
(110.3010) Imaging systems : Image reconstruction techniques

ToC Category:
Imaging Systems

Original Manuscript: December 27, 2013
Revised Manuscript: February 25, 2014
Manuscript Accepted: March 6, 2014
Published: March 19, 2014

Wen-Kai Yu, Ming-Fei Li, Xu-Ri Yao, Xue-Feng Liu, Ling-An Wu, and Guang-Jie Zhai, "Adaptive compressive ghost imaging based on wavelet trees and sparse representation," Opt. Express 22, 7133-7144 (2014)

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