OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Franco Gori
  • Vol. 31, Iss. 8 — Aug. 1, 2014
  • pp: 1716–1720

Realization of hybrid compressive imaging strategies

Yun Li, Aswin C. Sankaranarayanan, Lina Xu, Richard Baraniuk, and Kevin F. Kelly  »View Author Affiliations

JOSA A, Vol. 31, Issue 8, pp. 1716-1720 (2014)

View Full Text Article

Enhanced HTML    Acrobat PDF (1578 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



The tendency of natural scenes to cluster around low frequencies is not only useful in image compression, it also can prove advantageous in novel infrared and hyperspectral image acquisition. In this paper, we exploit this signal model with two approaches to enhance the quality of compressive imaging as implemented in a single-pixel compressive camera and compare these results against purely random acquisition. We combine projection patterns that can efficiently extract the model-based information with subsequent random projections to form the hybrid pattern sets. With the first approach, we generate low-frequency patterns via a direct transform. As an alternative, we also used principal component analysis of an image library to identify the low-frequency components. We present the first (to the best of our knowledge) experimental validation of this hybrid signal model on real data. For both methods, we acquire comparable quality of reconstructions while acquiring only half the number of measurements needed by traditional random sequences. The optimal combination of hybrid patterns and the effects of noise on image reconstruction are also discussed.

© 2014 Optical Society of America

OCIS Codes
(100.3190) Image processing : Inverse problems
(110.1758) Imaging systems : Computational imaging

ToC Category:
Imaging Systems

Original Manuscript: December 30, 2013
Revised Manuscript: March 12, 2014
Manuscript Accepted: April 15, 2014
Published: July 9, 2014

Yun Li, Aswin C. Sankaranarayanan, Lina Xu, Richard Baraniuk, and Kevin F. Kelly, "Realization of hybrid compressive imaging strategies," J. Opt. Soc. Am. A 31, 1716-1720 (2014)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk, “An information-theoretic approach to distributed compressed sensing,” in Proceedings of 45rd Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois,2005.
  2. D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.
  3. M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008). [CrossRef]
  4. M. A. Davenport, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk, “A simple proof that random matrices are democratic,” arXiv:0911.0736 (2009).
  5. T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009). [CrossRef]
  6. R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, “Model-based compressive sensing,” IEEE Trans. Inf. Theory 56, 1982–2001 (2010). [CrossRef]
  7. Y. C. Eldar, P. Kuppinger, and H. Bolcskei, “Block-sparse signals: uncertainty relations and efficient recovery,” IEEE Trans. Signal Process. 58, 3042–3054 (2010). [CrossRef]
  8. J. Huang and T. Zhang, “The benefit of group sparsity,” Annals Stat. 38, 1978–2004 (2010).
  9. V. Cevher, P. Indyk, C. Hegde, and R. G. Baraniuk, “Recovery of clustered sparse signals from compressive measurements,” (2009).
  10. A. Ashok and M. A. Neifeld, “Compressive imaging: hybrid measurement basis design,” J. Opt. Soc. Am. A 28, 1041–1050 (2011). [CrossRef]
  11. B. Adcock, A. C. Hansen, C. Poon, and B. Roman, “Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing,” arXiv:1302.0561 (2013).
  12. A. Ashok, L. Huang, and M. A. Neifeld, “Information optimal compressive sensing: static measurement design,” J. Opt. Soc. Am. A 30, 831–853 (2013). [CrossRef]
  13. E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006). [CrossRef]
  14. R. G. Baraniuk, “Compressive sensing [lecture notes],” IEEE Signal Process. Mag. 24(4), 118–121 (2007). [CrossRef]
  15. E. Van Den Berg and M. P. Friedlander, “SPGL1: a solver for large-scale sparse reconstruction,” http://www.cs.ubc.ca/labs/scl/spgl1 , 2007.
  16. C. Tsai and D. G. Nishimura, “Reduced aliasing artifacts using variable-density k-space sampling trajectories,” Magn. Reson. Med. 43, 452–458 (2000). [CrossRef]
  17. M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Process. Mag. 25(2), 72–82 (2008). [CrossRef]
  18. A. E. Waters, A. C. Sankaranarayanan, and R. G. Baraniuk, “SpaRCS: recovering low-rank and sparse matrices from compressive measurements,” in Advances in Neural Information Processing Systems (2011), pp. 1089–1097.
  19. “Columbus large image format (CLIF) 2007 dataset,” https://www.sdms.afrl.af.mil/index.php?collection=clif2007 .
  20. J. Liang and T. D. Tran, “Fast multiplierless approximations of the DCT with the lifting scheme,” IEEE Trans. Signal Process. 49, 3032–3044 (2001). [CrossRef]
  21. D. Bottisti and R. Muise, “Tree-based adaptive measurement design for compressive imaging under device constraints,” Proc. SPIE 8748, 874802 (2013). [CrossRef]
  22. A. C. Sankaranarayanan, C. Studer, and R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in IEEE International Conference on Computational Photography (ICCP) (2012), pp. 1–10.
  23. L. Xu, A. C. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, and K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2C.4.
  24. T. Goldstein, L. Xu, K. F. Kelly, and R. G. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.34056 (2013).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited