KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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A Novel Automatic Block-based Multi-focus Image Fusion via Genetic Algorithm

  • Yang, Yong (School of Information Technology, Jiangxi University of Finance and Economics) ;
  • Zheng, Wenjuan (School of Information Technology, Jiangxi University of Finance and Economics) ;
  • Huang, Shuying (School of Software and Communication Engineering, Jiangxi University of Finance and Economics)
  • Received : 2013.01.14
  • Accepted : 2013.06.04
  • Published : 2013.07.31
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Abstract

The key issue of block-based multi-focus image fusion is to determine the size of the sub-block because different sizes of the sub-block will lead to different fusion effects. To solve this problem, this paper presents a novel genetic algorithm (GA) based multi-focus image fusion method, in which the block size can be automatically found. In our method, the Sum-modified-Laplacian (SML) is selected as an evaluation criterion to measure the clarity of the image sub-block, and the edge information retention is employed to calculate the fitness of each individual. Then, through the selection, crossover and mutation procedures of the GA, we can obtain the optimal solution for the sub-block, which is finally used to fuse the images. Experimental results show that the proposed method outperforms the traditional methods, including the average, gradient pyramid, discrete wavelet transform (DWT), shift invariant DWT (SIDWT) and two existing GA-based methods in terms of both the visual subjective evaluation and the objective evaluation.

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References

  1. A. A. Goshtasby, S. G. Nikolov, "Image fusion: Advances in the state of the art," Information Fusion, vol. 8, no. 2, pp. 114-118, 2007. https://doi.org/10.1016/j.inffus.2006.04.001
  2. J. Dong, D. F. Zhuang, Y. H. Huang and J. Y. Fu, "Advances in Multi-Sensor Data Fusion: Algorithms and Applications," Sensors, vol. 9, no. 10, pp. 7771-7784, 2009. https://doi.org/10.3390/s91007771
  3. Y. Chai, H. F. Li, Z. F. Li, "Multifocus image fusion scheme using focused region detection and multiresolution," Optics Communications, vol. 284, no. 19, pp. 4376-4389, 2011. https://doi.org/10.1016/j.optcom.2011.05.046
  4. J. W Hu, S. T. Li, "The multiscale directional bilateral filter and its application to multisensor image fusion," Information Fusion, vol. 13, no. 3, pp. 196-206, 2012. https://doi.org/10.1016/j.inffus.2011.01.002
  5. R. S. Rosa, J. A. García, J. Fdez-Valdivia, "From computational attention to image fusion," Pattern Recognition Letters, vol. 32, no. 14, pp.1778-1795, 2011. https://doi.org/10.1016/j.patrec.2011.07.003
  6. B. Yang, S. T. Li, "Pixel-level image fusion with simultaneous orthogonal matching pursuit," Information Fusion, vol. 13, no. 1, pp.10-19, 2012. https://doi.org/10.1016/j.inffus.2010.04.001
  7. Q. Zhang, L. Wang, H. J. Li, Z. K. Ma, "Similarity-based multimodality image fusion with shiftable complex directional pyramid," Pattern Recognition Letters, vol. 32, no. 13, pp. 1544-1553, 2011. https://doi.org/10.1016/j.patrec.2011.06.002
  8. H. J. Zhao, Z. W. Shang, Y. Y. Tang, B. Fang, "Multi-focus image fusion based on the neighbor distance," Pattern Recognition, vol. 46, no. 3, pp. 1002-1011, 2013. https://doi.org/10.1016/j.patcog.2012.09.012
  9. S. Zheng, Y. Q. Sun, J. W. Tian, J. Liu, "Support Value Based Fusing Images With Different Focuses," in Proc. of the International Conference on Machine Learning and Cybernetics, pp. 5249-5254, 2005.
  10. S. T. Li, B. Yang, "Multi-focus image fusion by combining curvelet and wavelet transform," Pattern Recognition Letters, vol. 29, no. 9, pp. 1295-1301, 2008. https://doi.org/10.1016/j.patrec.2008.02.002
  11. P. J. Burt, E. H. Andelson, "The Laplacian pyramid as a compact image code," IEEE Transactions on Communications, vol. 31, no. 4, pp. 532-540, 1983. https://doi.org/10.1109/TCOM.1983.1095851
  12. P. J. Burt, "A gradient pyramid basis for pattern selective image fusion," in Proc. of the Society for Information Display Conference, pp. 467-470, 1992.
  13. A. Toet, "Image fusion by a ratio of low-pass pyramid," Pattern Recognition, vol. 9, no. 4, pp. 245-253, 1989. https://doi.org/10.1016/0167-8655(89)90003-2
  14. S. M. Mahbubur Rahman, M. Omair Ahmad, M. N. S. Swamy, "Contrast-based fusion of noisy images using discrete wavelet transform," IET Image Processing, vol. 4, no. 5, pp. 374-384, 2010. https://doi.org/10.1049/iet-ipr.2009.0163
  15. Y. Yang, "A Novel DWT Based Multi-focus Image Fusion Method," Procedia Engineering, vol. 24, pp. 177-181, 2011. https://doi.org/10.1016/j.proeng.2011.11.2622
  16. J. Tian, L. Chen, "Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure," Signal Processing, vol. 92, no. 9, pp. 2137-2146, 2012. https://doi.org/10.1016/j.sigpro.2012.01.027
  17. B. Yang, S. T. Li, "Multi-focus Image Fusion and Restoration with Sparse Representation," IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 4, pp. 884-892, 2010. https://doi.org/10.1109/TIM.2009.2026612
  18. W. Huang, Z. L. Jing, "Evaluation of focus measures in multi-focus image fusion," Pattern Recognition Letters, vol. 28, no. 4, pp. 493-500, 2007. https://doi.org/10.1016/j.patrec.2006.09.005
  19. M. Unser, "Texture classification and segmentation using wavelet frames," IEEE Trans. Image Processing, vol. 4, no. 11, pp.1549-560, 1995. https://doi.org/10.1109/83.469936
  20. S. T. Li, J. T. Kwok, Y. N. Wang, "Multifocus image fusion using artificial neural networks," Pattern Recognition Letters, vol. 23, no. 8, pp. 985-997, 2002. https://doi.org/10.1016/S0167-8655(02)00029-6
  21. S. T. Li, B. Yang, "Multi-focus image fusion using region segmentation and spatial frequency," Image and Vision Computing, vol. 26, no. 7, pp. 971-979, 2008. https://doi.org/10.1016/j.imavis.2007.10.012
  22. S. T. Li, Y. N. Wang, C. F. Zhang, "Feature of Human Vision System Based Multi-Focus Image Fusion," Acata Electronica Sinica, vol. 29, no. 12, pp. 1699-1701, 2001.
  23. J. Li, The Research on Methods of Multi-focus Image Fusion, Hunan University Press, 2006.
  24. X. M. Zhang, J. Q. Han, Y. Wang, "A Multifocus Image Fusion Algorithm for Adaptive Genetic Search," Journal of Electronics & Information Technology, vol. 28, no. 11, pp. 2054-2057, 2006.
  25. J. Kong, K. Y. Zheng, J. B. Zhang, X. Feng, "Multi-focus Image Fusion Using Spatial Frequency and Genetic Algorithm," International Journal of Computer Science and Network Security, vol. 8, no. 2, pp. 220-224, 2008.
  26. S. K. Nayar, Y. Nakagawa, "Shape from focus," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 8, pp. 824-831, 1994. https://doi.org/10.1109/34.308479
  27. C. S. Xydeas, V. Petrović, "Objective image fusion performance measure," Electronics Letters, vol. 36, no. 4, pp. 308-309, 2000. https://doi.org/10.1049/el:20000267
  28. S. T. Li, B. Yang, J. W. Hu, "Performance comparison of different multi-resolution transforms for image fusion," Information Fusion, vol. 12, no. 2, pp. 74-84, 2011. https://doi.org/10.1016/j.inffus.2010.03.002
  29. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.
  30. M. T. Ayvaz, H. Karahan, M. M. Aral, "Aquifer parameter and zone structure estimation using kernel-based fuzzy c-means clustering and genetic algorithm," Journal of Hydrology, vol. 343, no. 3-4, pp. 240-253, 2007. https://doi.org/10.1016/j.jhydrol.2007.06.018
  31. U. Maulik, "Medical Image Segmentation Using Genetic Algorithms," IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 2, pp. 166-173, 2009. https://doi.org/10.1109/TITB.2008.2007301
  32. A. G. Mahyari, M. Yazdi, "Fusion of panchromatic and multispectral images using temporal Fourier transform," IET Image Process., vol. 4, no. 4, pp. 255-260. 2010. https://doi.org/10.1049/iet-ipr.2009.0104
  33. Z. H. Li and H. Leung, "Fusion of Multispectral and Panchromatic Images Using a Restoration-Based Method," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 5, pp. 1482-1491, 2009. https://doi.org/10.1109/TGRS.2008.2005639

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