An application of compressive sensing for image fusion

References

• Achim , A. , Buxton , B. , Tzagkarakis , G. and Tsakalides , P. Compressive sensing for ultrasound rf echoes using α-stable distributions . Proceedings of the IEEE Conference on Engineering in Medicine and Biology Society . Buenos Aires, Argentina. pp. 4304 – 4307 .
   Google Scholar
• Akcakaya , M. and Tarokh , V. 2008 . A frame construction and a universal distortion bound for sparse representations . IEEE Trans. Signal Process. , 56 ( 6 ) : 2443 – 2450 .
   Google Scholar
• E. Candès and J. Romberg, l1-magic: Recovery of sparse signal via convex programming, Code package available at http://www.l1-magic.org
   Google Scholar
• Candès , E. and Tao , T. 2005 . Decoding by linear programming . IEEE Trans. Inform. Theory , 51 ( 12 ) : 4203 – 4215 .
   Web of Science ®Google Scholar
• Candès , E. , Romberg , J. and Tao , T. 2006 . Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information . IEEE Trans. Inform. Theory , 56 ( 2 ) : 489 – 509 .
   Google Scholar
• Candès , E. , Romberg , J. and Tao , T. 2006 . Stable signal recovery from incomplete and inaccurate measurements . Commun. Pure Appl. Math. , 59 ( 8 ) : 1207 – 1223 .
   Web of Science ®Google Scholar
• Chan , W. L. , Moraverc , M. L. , Baraniuk , R. G. and Mittleman , D. M. 2008 . Terahertz imaging with compressed sensing and phase retrieval . Opt. Lett. , 33 ( 9 ) : 974 – 976 .
   PubMedGoogle Scholar
• Deshmukh , M. and Bhosale , U. Image fusion and image quality assessment of fused images . Int. J. Image Process. , 4 ( 5 ) 484
   Google Scholar
• Donoho , D. L. 2006 . Compressed sensing . IEEE Trans. Inform. Theory , 52 ( 4 ) : 1289 – 1306 .
   Web of Science ®Google Scholar
• Duarte , M. , Davenport , M. , Takhar , D. , Laska , J. , Sun , T. , Kelly , K. and Baraniuk , R. 2008 . Single-pixel imaging via compressive sampling . IEEE Signal Process. Mag. , 25 ( 2 ) : 83 – 91 .
   Web of Science ®Google Scholar
• Egiazarian , K. , Foi , A. and Katkovnik , V. Compressed sensing image reconstruction via recursive spatially adaptive filtering . Proceedings of the IEEE International Conference on Image Processing . San Antonio, Texas, USA. Vol. 1 , pp. 549 – 552 .
   Google Scholar
• Kim , S. J. , Koh , K. , Lustig , M. and Boyd , S. An efficient method for compressed sensing . Proceedings of the IEEE International Conference on Image Processing . San Antonio, Texas, USA. Vol. 3 , pp. 117 – 120 .
   Google Scholar
• Li , X. and Qin , S. 2010 . Efficient fusion for infrared and visible images based on compressive sensing principle . IET Image Process. , 5 ( 2 ) : 141 – 147 .
   Google Scholar
• Lloyd , S. P. 1982 . Least squares quantization in PCM . IEEE Trans. Inform. Theory , 28 ( 2 ) : 129 – 137 .
   Web of Science ®Google Scholar
• Loza , A. , Bull , D. , Canagarajah , N. and Achim , A. 2010 . Statistical model-based fusion of noisy images in the wavelet domain . Comput. Vis. Image Understand. , 114 : 54 – 65 .
   Web of Science ®Google Scholar
• Marr , D. 1982 . Vision , New York : W. H. Freeman .
   Google Scholar
• Nencini , F. , Garzelli , A. , Baronti , S. and Alparone , L. 2007 . Remote sensing image fusion using the curvelet transform . Inform. Fusion , 8 ( 2 ) : 143 – 156 .
   Web of Science ®Google Scholar
• Petrovic , V. and Xydeas , C. On the effects of sensor noise in pixel-level image fusion performance . Proceedings of the International Conference on Image Fusion . Paris, France. Vol. 2 , pp. 14 – 19 .
   Google Scholar
• Piella , G. 2003 . A general framework for multiresolution image fusion: from pixels to regions . Inform. Fusion , 4 : 259 – 280 .
   Google Scholar
• Piella , G. and Heijmans , H. A new quality metric for image fusion . Proceedings of the IEEE Conference on Image Processing . Barcelona, Spain. Vol. 2 , pp. 173 – 176 .
   Google Scholar
• Provost , J. and Lesage , F. 2009 . The application of compressed sensing for photo-acoustic tomography . IEEE Trans. Med. Imag. , 28 ( 4 ) : 585 – 594 .
   PubMed Web of Science ®Google Scholar
• Shannon , C. E. 1984 . Communication in the Presence of Noise . Proc. IEEE. , 72 : 1192 – 1201 .
   Web of Science ®Google Scholar
• The online resource for research in image fusion. Available at http://www.imagefusion.org/
   Google Scholar
• The Berkeley Segmentation Dataset Benchmark, University of California, Berkeley, CA. Available at http://www.eecs.berkeley.edu/Research/Projects/CS-/vision/grouping/segbench/, Jul. 2003
   Google Scholar
• Trzasko , J. and Manduca , A. 2009 . Highly undersampled magnetic resonance image reconstruction via homotopic l 0-minimization . IEEE Trans. Med. Imag. , 28 ( 1 ) : 106 – 121 .
   PubMedGoogle Scholar
• Tsaig , Y. and Donoho , D. L. 2005 . Extensions of compressed sensing . Signal Process. , 86 : 549 – 571 .
   Google Scholar
• Wakin , M. B. , Laska , J. N. , Duarte , M. F. , Baron , D. , Sarvotham , S. , Takhar , D. , Kelly , K. F. and Baraniuk , R. G. An architecture for compressive imaging . Proceedings of the IEEE International Conference on Image Processing . Atlanta, Georgia, USA. pp. 1273 – 1276 .
   Google Scholar
• Wan , T. and Qin , Z. An application of compressive sensing for image fusion . The ACM International Conference on Image and Video Retrieval . Xi'an, China. pp. 3 – 9 .
   Google Scholar
• Wan , T. , Canagarajah , N. and Achim , A. Region-based multisensor image fusion using generalized Gaussian distribution . International Workshop on Nonlinear Signature and Image Processing . Bucharest, Romania.
   Google Scholar
• Wan , T. , Canagarajah , N. and Achim , A. Compressive image fusion . Proceedings of the IEEE International Conference Image Processing . San Diego, California, USA. pp. 1308 – 1311 .
   Google Scholar
• Wan , T. , Tzagkarakis , G. , Tsakalides , P. , Canagarajah , N. and Achim , A. Context enhancement through image fusion: A multiresolution approach based on convolution of cauchy distributions . Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing . Las Vegas, Nevada, USA. pp. 1309 – 1312 .
   Google Scholar
• Wan , T. , Canagarajah , N. and Achim , A. 2009 . Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients . IEEE Trans. Multimedia , 11 ( 4 ) : 624 – 633 .
   Google Scholar
• Wang , Z. and Bovik , A. 2002 . A universal image quality index . Signal Process. Lett. , 9 ( 3 ) : 81 – 84 .
   Web of Science ®Google Scholar
• Wang , Z. , Ziou , D. , Armenakis , C. , Li , D. and Li , Q. 2005 . A comparative analysis of image fusion methods . IEEE Trans. Geosci. Remote Sens. , 43 ( 6 ) : 1391 – 1402 .
   Web of Science ®Google Scholar
• Wang , A. , Sun , H. and Guan , Y. The application of wavelet transform to multi-modality medical image fusion . Proceedings of the IEEE Conference on Networking, Sensing and Control . Ft. Lauderdale, Florida, USA. pp. 270 – 274 .
   Google Scholar