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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  4/2013 - 14

Combined Sparsifying Transforms for Compressive Image Fusion

WU, C. See more information about WU, C. on SCOPUS See more information about WU, C. on IEEExplore See more information about WU, C. on Web of Science, WANG, H. See more information about  WANG, H. on SCOPUS See more information about  WANG, H. on SCOPUS See more information about WANG, H. on Web of Science, XU, X. See more information about  XU, X. on SCOPUS See more information about  XU, X. on SCOPUS See more information about XU, X. on Web of Science, ZHAO, L. See more information about ZHAO, L. on SCOPUS See more information about ZHAO, L. on SCOPUS See more information about ZHAO, L. on Web of Science
 
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Download PDF pdficon (821 KB) | Citation | Downloads: 539 | Views: 2,872

Author keywords
compressive sensing, combined sparsifying transforms, image fusion

References keywords
processing(14), image(13), sensing(11), signal(8), fusion(8), sparse(6), information(5), imaging(4), icip(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-11-30
Volume 13, Issue 4, Year 2013, On page(s): 79 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.04014
Web of Science Accession Number: 000331461300014
SCOPUS ID: 84890250999

Abstract
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In this paper, we present a new compressive image fusion method based on combined sparsifying transforms. First, the framework of compressive image fusion is introduced briefly. Then, combined sparsifying transforms are presented to enhance the sparsity of images. Finally, a reconstruction algorithm based on the nonlinear conjugate gradient is presented to get the fused image. The simulations demonstrate that by using the combined sparsifying transforms better results can be achieved in terms of both the subjective visual effect and the objective evaluation indexes than using only a single sparsifying transform for compressive image fusion.


References | Cited By  «-- Click to see who has cited this paper

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[4] M. F. Duarte, M. A. Davenport, D. Takhar, et al. "Single-pixel imaging via compressive sampling," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 83-91, 2008.
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[5] D. Giacobello, M. G. Christensen, M. N. Murthi, S. H. Jensen, and M. Moonen, "Retrieving Sparse Patterns Using a Compressed Sensing Framework: Applications to Speech Coding Based on Sparse Linear Prediction," IEEE Signal Processing Letters, vol. 17, no. 1, pp. 103-106, 2010.
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 70]


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[CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 111]


[7] M. Fira, L. Goras, C. Barabasa, and N. Cleju, "On ECG Compressed Sensing using Specific Overcomplete Dictionaries," Advances in Electrical and Computer Engineering, vol. 10, no. 4, pp. 23-28, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 25] [SCOPUS Times Cited 29]


[8] M. Fira, L. Goras, "A New Method for EEG Compressive Sensing," Advances in Electrical and Computer Engineering, vol. 12, no. 4, pp. 71-76, 2012.
[CrossRef] [Full Text] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]


[9] J. Romberg, "Imaging via compressive sampling," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 14-20, 2008.
[CrossRef] [Web of Science Times Cited 642] [SCOPUS Times Cited 819]


[10] T. Wan, N. Canagarajah, and A. Achim, "Compressive image fusion," IEEE International Conference on Image Processing, pp. 1308-1311, 2008.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 87]


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[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 38]


[12] X. Li, S. Y. Qin, "Efficient fusion for infrared and visible images based on compressive sensing principle," IET Image Processing, vol. 5, no. 2, pp. 141-147, 2011.
[CrossRef] [Web of Science Times Cited 59] [SCOPUS Times Cited 86]


[13] X. Y. Luo, J. Zhang, J. Y. Yang, and Q. H. Dai, "Image fusion in compressed sensing," IEEE International Conference on Image Processing, pp. 2205-2208, 2009.
[CrossRef] [SCOPUS Times Cited 42]


[14] X. Qu, X. Cao, D. Guo, C. Hu, and Z. Chen, "Combined sparsifying transforms for compressed sensing MRI," Electronics Letters, vol. 46, no. 2, pp. 121-123, 2010.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 52]


[15] M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, "Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems," IEEE Journal of Selected Topics in Signal Processing, vol. 1, no. 4, pp. 586-597, 2007.
[CrossRef] [Web of Science Times Cited 2125] [SCOPUS Times Cited 2712]


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[CrossRef] [Web of Science Times Cited 2496] [SCOPUS Times Cited 3604]


[17] Y. Lu, M. N. Do, "A new contourlet transform with sharp frequency localization," IEEE International Conference on Image Processing, pp. 1629-1632, 2006.
[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 155]


[18] Q. Zhang, B. L. Guo, "Multifocus image fusion using the nonsubsampled contourlet transform," Signal Processing, vol. 89, no. 7, pp. 1334-1346, 2009.
[CrossRef] [Web of Science Times Cited 470] [SCOPUS Times Cited 563]


[19] M. Lustig, D. Donoho, and J. M. Pauly, "Sparse MRI: the application of compressed sensing for rapid MR Imaging," Magnetic Resonance in Medicines, vol. 58, no. 10, pp. 1182-1195, 2007.
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[CrossRef] [Web of Science Times Cited 1162] [SCOPUS Times Cited 1334]


[21] W. W. Hager, H. Zhang. "A survey of nonlinear conjugate gradient methods," Pacific journal of Optimization, vol. 2, no. 1, pp. 35-58, 2006.

[22] G. H. Qu, D. L. Zhang, and P. F. Yan, "Information measure for performance of image fusion," Electronics Letters, vol. 38, no. 7, pp. 313-315, 2002.
[CrossRef] [Web of Science Times Cited 705] [SCOPUS Times Cited 945]


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[CrossRef] [SCOPUS Times Cited 69]


[24] M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311-4322, 2006.
[CrossRef]




References Weight

Web of Science® Citations for all references: 41,156 TCR
SCOPUS® Citations for all references: 51,597 TCR

Web of Science® Average Citations per reference: 1,646 ACR
SCOPUS® Average Citations per reference: 2,064 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-06-29 11:05 in 149 seconds.




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