Abstract
Based on the compressive sensing theory (CS), various compressive imaging (CI) systems have been developed. Meanwhile, image fusion methods that directly perform on the measurements from multiple CI sensors are also investigated in literatures. In this paper, we presented a multi-focus image fusion method in compressive sensing domain. The main contribution is to introduce a novel clarity level of the random CI measurements without prior geometric information. The CI measurements are sparsely represented with DCT bases which are also projected into the CS domain. Then the sparse coefficients responding to DCT bases are used to guide the fusion of CI measurements of CI sensors. Finally, the fused images are obtained with CS recovery algorithm based on the block compressive sensing (BCS) theory. The simulation results validate the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Li, H., Chai, Y., Yin, H., et al.: Multifocus image fusion and denoising scheme based on homogeneity similarity. Optics Communications 285(2), 91–100 (2012)
Qu, G.H., Zhang, D.L., Yan, P.F.: Medical image fusion by wavelet transform modulus maxima. Optics Express 9(4), 184–190 (2001)
Liu, Z., Tsukada, K., Hanasaki, K., et al.: Image fusion by using steerable pyramid. Pattern Recognition Letters 22(9), 929–939 (2001)
Petrovic, V.S., Xydeas, C.S.: Gradient-based multiresolution image fusion. IEEE Transactions on Image Processing 13(2), 228–237 (2004)
Pajares, G., Manuelde la Cruz, J.: A wavelet-based image fusion tutorial. Pattern Recognition 37(9), 1855–1872 (2004)
Chu, H., Zhu, W.L.: Image fusion algorithms using discrete cosine transform. Optics and Precision Engineering 14(2), 266–273 (2006)
Zhang, Q., Guo, B.: Multifocus image fusion using the nonsubsampled contourlet transform. Signal Processing 89(7), 1334–1346 (2009)
Baraniuk, R.: Compressive sensing. IEEE Signal Processing Magazine 24(4), 118–121 (2007)
Wan, T., Canagarajah, N., Achim, A.: Compressive image fusion. In: Proceedings of 15th IEEE International Conference on Image Processing, pp. 1308–1311 (2008)
Han, J., Loffeld, O., Hartmann, K., et al.: Multi image fusion based on compressive sensing. In: Proceedings of the International Conference on Audio Language and Image Processing, pp. 1463–1469 (2010)
Luo, X., Zhang, J., Yang, J.Y., Dai, Q.H.: Image fusion in compressed sensing. In: Proceedings of 16th IEEE International Conference on Image Processing, Piscataway, NJ, pp. 2205–2208 (2009)
Luo, X., Yang, J., Dai, Q., et al.: Classification-based image-fusion framework for compressive imaging. Journal of Electronic Imaging 19(3), 033009-1–033009-14 (2010)
Yang, B., Li, S.T.: Pixel-level image fusion with simultaneous orthogonal matching pursuit. Information Fusion 13(1), 10–19 (2012)
Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: Multi-focus image fusion for visual sensor networks in DCT domain. Computers & Electrical Engineering 37(5), 789–797 (2011)
Gan, L.: Block compressed sensing of natural images. In: Proceedings of 15th IEEE International Conference on Digital Signal Processing, pp. 403–406 (2007)
Mun, S., Fowler, J.E.: Block compressed sensing of images using directional transforms. In: Proceedings of 16th IEEE International Conference on Image Processing, pp. 3021–3024 (2009)
Baraniuk, R.G.: Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine 25(2), 83–91 (2008)
Candès, E.J.: Compressive sampling. In: Proceedings of the International Congress of Mathematicians, vol. 3, pp. 1433–1452 (2006)
Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory 53(12), 4655–4666 (2007)
Mun, S., Fowler, J.E.: Block compressed sensing of images using directional transforms. In: Proceedings of 16th IEEE International Conference on Image Processing, pp. 3021–3024 (2009)
Piella, G.: H Heijmans. A new quality metric for image fusion. In: Proceedings of the International Conference on Image Processing, vol. 2, pp. III-173–III-176 (2003)
Xydeas, C.S., Petrović Objective, V.: image fusion performance measure. Electronics Letters 36(4), 308–309 (2000)
Kingsbury, N.: Complex wavelets for shift invariant analysis and filtering of signals. Applied and Computational Harmonic Analysis 10(3), 234–253 (2001)
Li, S.T., Kang, X.D., Hu, J.W.: Image fusion with guided filtering. IEEE Transactions on Image Processing 22(7), 2864–2875 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, F., Yang, B., Huang, Z. (2014). Compressive Sensing Multi-focus Image Fusion. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-662-45643-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45642-2
Online ISBN: 978-3-662-45643-9
eBook Packages: Computer ScienceComputer Science (R0)