OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 4 — Feb. 25, 2013
  • pp: 5182–5197

Regional multifocus image fusion using sparse representation

Long Chen, Jinbo Li, and C. L. Philip Chen  »View Author Affiliations


Optics Express, Vol. 21, Issue 4, pp. 5182-5197 (2013)
http://dx.doi.org/10.1364/OE.21.005182


View Full Text Article

Enhanced HTML    Acrobat PDF (4925 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Due to the nature of involved optics, the depth of field in imaging systems is usually constricted in the field of view. As a result, we get the image with only parts of the scene in focus. To extend the depth of field, fusing the images at different focus levels is a promising approach. This paper proposes a novel multifocus image fusion approach based on clarity enhanced image segmentation and regional sparse representation. On the one hand, using clarity enhanced image that contains both intensity and clarity information, the proposed method decreases the risk of partitioning the in-focus and out-of-focus pixels in the same region. On the other hand, due to the regional selection of sparse coefficients, the proposed method strengthens its robustness to the distortions and misplacement usually resulting from pixel based coefficients selection. In short, the proposed method combines the merits of regional image fusion and sparse representation based image fusion. The experimental results demonstrate that the proposed method outperforms six recently proposed multifocus image fusion methods.

© 2013 OSA

OCIS Codes
(100.0100) Image processing : Image processing
(350.2660) Other areas of optics : Fusion
(100.4994) Image processing : Pattern recognition, image transforms

ToC Category:
Image Processing

History
Original Manuscript: December 17, 2012
Revised Manuscript: January 21, 2013
Manuscript Accepted: February 10, 2013
Published: February 22, 2013

Virtual Issues
Vol. 8, Iss. 3 Virtual Journal for Biomedical Optics

Citation
Long Chen, Jinbo Li, and C. L. Philip Chen, "Regional multifocus image fusion using sparse representation," Opt. Express 21, 5182-5197 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-4-5182


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. H. Li, B. Manjunath, and S. K. Mitra, “Multisensor image fusion using the wavelet transform,” Graph. Model. Im. Proc.57(3), 235–245 (1995) [CrossRef]
  2. H. Li, Y. Chai, H. Yin, and G. Liu, “Multifocus image fusion and denoising scheme based on homogeneity similarity,” Opt. Commun.285(2), 91–100 (2012). [CrossRef]
  3. Y. Song, M. Li, Q. Li, and L. Sun, “A new wavelet based multi-focus image fusion scheme and its application on optical microscopy,” in Proceedings of IEEE Conference on Robotics and Biomimetics (Institute of Electrical and Electronics Engineers, Kunming, China, 2006), pp. 401–405.
  4. Y. Chen, L. Wang, Z. Sun, Y. Jiang, and G. Zhai, “Fusion of color microscopic images based on bidimensional empirical mode decomposition,” Opt. Express18(21), 21757–21769 (2010). [CrossRef] [PubMed]
  5. Q. Guihong, Z. Dali, and Y. Pingfan, “Medical image fusion by wavelet transform modulus maxima,” Opt. Express9(4), 184–190 (2001). [CrossRef] [PubMed]
  6. T. Stathaki, Image Fusion: Algorithms and Applications (Academic Press, 2008).
  7. X. Bai, F. Zhou, and B. Xue, “Fusion of infrared and visual images through region extraction by using multi-scale center-surround top-hat transform,” Opt. Express19(9), 8444–8457 (2011). [CrossRef] [PubMed]
  8. H. Hariharan, “Extending Depth of Field via Multifocus Fusion,” PhD Thesis, The University of Tennessee, Knoxville, 2011.
  9. H. B. Mitchell, Image Fusion: Theories, Techniques and Applications (Springer, 2010). [CrossRef]
  10. J. Tian, L. Chen, L. Ma, and W. Yu, “Multi-focus image fusion using a bilateral gradient-based sharpness criterion,” Opt. Commun.284(1), 80–87 (2011). [CrossRef]
  11. Y. Zhang and L. Ge, “Efficient fusion scheme for multi-focus images by using blurring measure,” Digital Sig. Process.19(2), 186–193 (2009). [CrossRef]
  12. J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell.22(8), 888–905 (2000) [CrossRef]
  13. A. Bleau and L.J. Leon, “Watershed-based segmentation and region merging” Comput. Vis. Image Und.77(3), 317–370 (2000). [CrossRef]
  14. N.R. Pal and S.K. Pal, “A review on image segmentation techniques” Pattern Recogn.26(9), 1277–1294 (1993) [CrossRef]
  15. S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image Vis. Comput.26(7), 971–979 (2008). [CrossRef]
  16. L. Guo, M. Dai, and M. Zhu, “Multifocus color image fusion based on quaternion curvelet transform,” Opt. Express20(17), 18846–18860 (2012). [CrossRef] [PubMed]
  17. X. Qu, J. Yan, and G. Yang, “Multifocus image fusion method of sharp frequency localized contourlet transform domain based on sum-modified-laplacian,” Opt. Precis. Eng.17(5), 1203–1212 (2009).
  18. B. Yang and S. Li, “Multifocus image fusion and restoration with sparse representation,” IEEE Trans. Instrum. Meas.59(4), 884–892 (2010). [CrossRef]
  19. Z. Wang, Y. Ma, and J. Gu, “Multi-focus image fusion using PCNN,” Pattern Recogn.43(6), 2003–2016 (2010). [CrossRef]
  20. S. Li, J. T. Kwok, and Y. Wang, “Combination of images with diverse focuses using the spatial frequency,” Inf. Fusion26(7), 169–176 (2001). [CrossRef]
  21. K. Huang and S. Aviyente, “Sparse representation for signal classification,” Adv. Neural Inf. Process. Syst.19, 609–616 (2007).
  22. D. L. Donoho, “Compressed sensing,” IEEE Trans. Inform. Theory.52(4), 1289–1306 (2006). [CrossRef]
  23. B. A. Olshausen, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature (London)381, 607–609 (1996). [CrossRef]
  24. R. Rubinstein, A. M. Bruckstein, and M. Elad, “Dictionaries for sparse representation modeling,” Proc. IEEE98(6), 1045–1057 (2010). [CrossRef]
  25. G. Davis, S. Mallat, and M. Avellaneda, “Adaptive greedy approximations,” Constr. Approx.13(1), 57–98 (1997).
  26. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse sepresentation,” IEEE Trans. Sig. Proces.54, (11)4311–4322 (2006) [CrossRef]
  27. C. Xydeas and V. Petrovic, “Objective image fusion performance measure,” Electron. Lett.36(4), 308–309 (2000). [CrossRef]
  28. J. Huang, T. Zhang, and D. Metaxas, “Learning with structured sparsity,” Proceedings of the 26th Annual International Conference on Machine Learning, 417–424 (2009).
  29. J. Huang, X. Huang, and D. Metaxas, “Learning with dynamic group sparsity,” Proceedings of the 12th International Conference on Computer Vision, 64–71 (2009).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited