JCC  Vol.2 No.9 , July 2014
Efficient Compressive Multi-Focus Image Fusion
Author(s) Chao Yang, Bin Yang*
Abstract

Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.


Cite this paper
Yang, C. and Yang, B. (2014) Efficient Compressive Multi-Focus Image Fusion. Journal of Computer and Communications, 2, 78-86. doi: 10.4236/jcc.2014.29011.
References

[1]   Li, S.T. and Yang, B. (2008) Multi-Focus Image Fusion by Combining Curvelet and Wavelet Transform. Pattern Recognition Letters, 29, 1295-1301. http://dx.doi.org/10.1016/j.patrec.2008.02.002

[2]   Wang, W.C. (2011) A Multi-Focus Image Fusion Method Based on Laplacian Pyramid. Journal of Computational Chemistry, 6, 2559-2566. http://dx.doi.org/10.4304/jcp.6.12.2559-2566

[3]   Zhang, Q. and Guo, B.L. (2009) Multi-Focus Image Fusion Using the Nonsubsampled Contourlet Transform. Signal Process, 89, 1334-1346. http://dx.doi.org/10.1016/j.sigpro.2009.01.012

[4]   Piella, G. (2003) A General Framework for Multi-Resolution Image Fusion: From Pixels to Regions. Information Fusion, 4, 259-280.

[5]   Smith, M.I. and Heather, J.P. (2005) A Review of Image Fusion Technology, Defense and Security. International So- ciety for Optics and Photonics, 29-45.

[6]   Toet, A. (1989) Image Fusion by a Ratio of Lowpass Pyramid. Pattern Pattern Recognition Letters, 9, 245-253.

[7]   Li, S.T., Kwok, J.T. and Wang, Y.N. (2001) Combination of Images with Diverse Focuses Using the Spatial Frequency. Information Fusion, 2, 169-176. http://dx.doi.org/10.1016/S1566-2535(01)00038-0

[8]   Zhang, Y. and Ge, L. (2009) Efficient Fusion Scheme for Multi-Focus Images by Using Blurring Measure. Digital Signal Processing, 19, 186-193. http://dx.doi.org/10.1016/j.dsp.2008.11.002

[9]   Petrovic, V.S., Xydeas, C.S. (2004) Gradient-Based Multiresolution Image Fusion. IEEE Transactions on Image Processing, 13, 228-237. http://dx.doi.org/10.1109/TIP.2004.823821

[10]   De, I. and Chanda, B. (2013) Multi-Focus Image Fusion Using a Morphology-Based Focus Measure in a Quad-Tree Structure. Information Fusion, 14, 136-146. http://dx.doi.org/10.1016/j.inffus.2012.01.007

[11]   Donoho, D.L. (2006) Compressed Sensing. IEEE Transactions on Information Theory, 52, 1289-1306. http://dx.doi.org/10.1109/TIT.2006.871582

[12]   Candes, E.J. and Wakin, M.B. (2008) An Introduction to Compressive Sampling. IEEE Signal Processing Magazine, 25, 21-30. http://dx.doi.org/10.1109/MSP.2007.914731

[13]   Candes, E. and Tao, T. (2006) Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strate- gies? IEEE Transactions on Information Theory, 52, 5406-5425. http://dx.doi.org/10.1109/TIT.2006.885507

[14]   Baraniuk, R.G. (2008) Single-Pixel Imaging via Compressive Sampling. IEEE Signal Processing Magazine.

[15]   Wan, T., Canagarajah, N. and Achim, A. (2008) Compressive Image Fusion, Image Processing. 15th IEEE Interna- tional Conference, 1308-1311.

[16]   Gan, L., Do, T.T. and Tran, T.D. (2008) Fast Compressive Imaging Using Scrambled Block Hadamard Ensemble. In: EUSIPCO, Lausanne, Switzerland.

[17]   Luo, X.Y. and Zhang, J. (2010) Classification-Based Image-Fusion Framework for Compressive Imaging. Journal of Electronic Imaging, 19, Article ID: 033009. http://dx.doi.org/10.1117/1.3478879

[18]   Candes, E.J., Romberg, J.K. and Tao, T. (2006) Stable Signal Recovery from Incomplete and Inaccurate Measurements. Communications on Pure and Applied Mathematics, 59, 1207-1223. http://dx.doi.org/10.1002/cpa.20124

[19]   Sendur, L. and Selesnick, I.W. (2002) Bivariate Shrinkage Functions for Wavelet-Based Denoising Exploiting Inter Scale Dependency. IEEE Transactions on Signal Processing, 50, 2744-2756. http://dx.doi.org/10.1109/TSP.2002.804091

[20]   Liao, C., Li, S. and Luo, Z. (2007) Gene Selection Using Wilcoxon Rank Sum Test and Support Vector Machine for Cancer Classification. Computational Intelligence and Security, Springer, Berlin, Heidelberg, 57-66.

[21]   Gan, L. (2007) Block Compressed Sensing of Natural Images, Digital Signal Processing. 15th International Conference on Digital Signal Processing, 403-406.

[22]   Xydeas, C. and Petrovic, V. (2000) Objective Image Fusion Performance Measure. Electronics Letters, 36, 308-309. http://dx.doi.org/10.1049/el:20000267

[23]   Piella, G. and Heijmans, H. (2003) A New Quality Metric for Image Fusion. IEEE International Conference on Image Processing, Barcelona, 173-176.

[24]   Kingsbury, N. (2001) Complex Wavelets for Shift Invariant Analysis and Filtering of Signals. Applied and Computational Harmonic Analysis, 10, 234-253. http://dx.doi.org/10.1006/acha.2000.0343

 
 
Top