Share This Article:

A Distributed Compressed Sensing for Images Based on Block Measurements Data Fusion

Abstract Full-Text HTML Download Download as PDF (Size:314KB) PP. 134-139
DOI: 10.4236/jsea.2012.512B026    2,885 Downloads   4,140 Views   Citations

ABSTRACT

Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a novel method called distributed compressed sensing for image using block measurements data fusion. Firstly, original image is divided into small blocks and each block is sampled independently using the same measurement operator, to obtain the smaller encoded sparser coefficients and stored measurements matrix and its vectors.  Secondly, original image is reconstructed using the block measurements fusion and recovery transform. Finally, several numerical experiments demonstrate that our method has a much lower data storage and calculation cost as well as high quality of reconstruction when compared with other existing schemes. We believe it is of great practical potentials in the network communication as well as pattern recognition domain.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

H. Chen and J. Liu, "A Distributed Compressed Sensing for Images Based on Block Measurements Data Fusion," Journal of Software Engineering and Applications, Vol. 5 No. 12B, 2012, pp. 134-139. doi: 10.4236/jsea.2012.512B026.

References

[1] Varma,K.Bell.A.JPEG2000-choices and tradeoffs for encoders[J].Signal Processing Magazine, IEEE Nov.2004 Volume:21,Issue:6:70-75.
[2] DONOHO David L. Compressed Sensing[J].IEEE Trans Inform Theory,2006,52:1289-1306.
[3] CANDéS E, ROMBERG J, TAO T. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information[J]. IEEE Transactions on Infor-mation Theory,2006, 52(2): 489-509.
[4] BARANIUK R, CEVHER V, DUARTE M, et al. Model-based Com-pressive Sensing[J].IEEE Trans. Inform. ThEory,2010, 56(4):1982-2001.
[5] Baron, D., Wakin, M.B., Duarte, M.F. Sarvotham,S., Baraniuk, R.G. Distributed com-pressed sensing.(2005) Available at http://www.dsp.rice.edu/cs.
[6] M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Processing Magazine, vol. 2, no. 25, pp. 83–91, 2008.
[7] Sungkwang Mun, James E. Fowler. Block Compressed Sensing of images using directional trans-forms. Proceedings of the International Conference on Image Processing, 2009, 3021-3024.
[8] Baraniuk R. Compressive sensing[J]. IEEE Signal Processing Maga-zine, 2007, 24(4): 118-121
[9] BOURGAIN J, DIL-WORTH S, FORD K., et al. Explicit Constructions of Rip Matrices and Related Problems[J]. Duke Math. J,2011,159:145-185.
[10] M. Davenport ,M. Wakin. Analysis of orthogonal matching pursuit using the re-stricted isometry property. IEEE Trans Inform Theory, 2010,56(9):4395-4401.
[11] M. Davenport ,M. Wakin. Analysis of orthogonal matching pursuit using the re-stricted isometry property. IEEE Trans Inform Theory, 2010,56(9):4395-4401.
[12] T. Blumensath , M. Davies. Iterative hard thresholding for compressive sensing.Appl Comput Harmon Anal,2009, 27(3):265-274.
[13] T. Blumensath and M. Davies. Gradient pursuits. IEEE Trans. Signal Processing, 2008,56(6):2370-2382.
[14] E. Candes, B. Recht. Exact matrix completion via convex optimization. Found Comput Math, 2009,9(6):717-772.
[15] Chartrand R.Exact recon-struction of sparse signals via non-convex minimiza-tion[J].IEEE Signal Processing Letters,2007,14(10),707- 710.
[16] Gan L.Block compressed sensing of natural images[C]//Proc Int Conf on Digital Signal Processing, Cardiff, UK, 2007.
[17] Yaakov Tsaig, David L.Donoho. Extensions of Compressed Sensing. Signal Processing, 86(2006) 549-571.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.