A Novel Architecture for Wavelet based Image Fusion
In this paper, we focus on the fusion of images from
different sources using multiresolution wavelet transforms. Based on
reviews of popular image fusion techniques used in data analysis,
different pixel and energy based methods are experimented. A novel
architecture with a hybrid algorithm is proposed which applies pixel
based maximum selection rule to low frequency approximations and
filter mask based fusion to high frequency details of wavelet
decomposition. The key feature of hybrid architecture is the
combination of advantages of pixel and region based fusion in a
single image which can help the development of sophisticated
algorithms enhancing the edges and structural details. A Graphical
User Interface is developed for image fusion to make the research
outcomes available to the end user. To utilize GUI capabilities for
medical, industrial and commercial activities without MATLAB
installation, a standalone executable application is also developed
using Matlab Compiler Runtime.
[1] M. Sasikala and N. Kumaravel, "A comparative analysis of featurebased
image fusion methods," Information Technology Journal,
6(8):1224- 1230, 2007.
[2] J. Daugman and C. Downing, "Gabor wavelets for statistical pattern
recognition," The handbook of brain theory and neural networks, M. A.
Arbib, ed. Cambridge, MA, USA: MIT Press, 1998, pp.414-420.
[3] S. Mallat, "Wavelets for a vision," Proceedings of the IEEE, New York
Univ., NY, 84(4):604-614, April 1996.
[4] A. Wang, H. Sun and Y. Guan, "The application of wavelet transform to
multimodality medical image fusion," Proc. IEEE International
Conference on Networking, Sensing and Control (ICNSC), Ft.
Lauderdale, Florida, 2006, pp.270-274.
[5] O. Rockinger, "Pixel-level fusion of image sequences using wavelet
frames," Proc. of the 16th Leeds Applied Shape Research Workshop,
Leeds University Press, 1996, 149-154.
[6] H. Li, B. S. Manjunath, and S. K. Mitra, "Multisensor image fusion
using the wavelet transform," Graphical Models and Image Processing,
57(3):235-245, May 1995.
[7] M. Jian, J. Dong and Y. Zhang, "Image fusion based on wavelet
transform," Proc., 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Distributed
Computing,,Qingdao, China, July 2007.
[8] Z. Yingjie and G. Liling, "Region-based image fusion approach using
iterative algorithm," Proc. Seventh IEEE/ACIS International Conference
on Computer and Information Science(ICIS), Oregon, USA, May 2008.
[9] H. Zhang, L. Liu and N. Lin, "A novel wavelet medical image fusion
method," International Conference on Multimedia and Ubiquitous
Engineering (MUE-07), Seoul, Korea, April 2007.
[10] V. Petrovic, "Multilevel image fusion," Proceedings of SPIE, 5099:87-
96, 2003.
[11] Y. Zheng, X. Hou, T. Bian and Z. Qin, "Effective image fusion rules of
multiscale image decomposition," Proc. of 5th International Symposium
on Image and Signal Processing and Analysis (ISPA07), Istanbul,
Turkey, September 2007, pp. 362-366.
[12] J. Gao, Z. Liu and T. Ren, "A new image fusion scheme based on
wavelet transform," Proc., 3rd International Conference on Innovative
Computing,Information and Control, Dalian, China, June 2008.
[13] I. Daubechies, "The wavelet transform, time-frequency localization and
signal analysis," IEEE Trans. Info. Theory, 36:961-1005, 1990.
[14] M. Vetterli and C.Herley, "Wavelets and filter banks: theory and
design," IEEE Transactions on Signal Processing, 40(9):2207-2232,
September 1992.
[15] S. G. Mallat, "A Theory for multiresolution signal decomposition - the
wavelet representation," IEEE Transactions on Pattern Analysis and
Machine Intelligence, 11(7):674-693, July 1989.
[16] R. C. Luo and M. G. Kay, "Data fusion and sensor integration: state of
the art 1990s," Data Fusion in Robotics and Machine Intelligence, M. A.
Abidi and R. C. Gonzalez eds., Academic Press, San Diego, 1992, pp.7-
135.
[17] Y. Du, P. W. Vachon, and J. J. V. Sanden, "Satellite image fusion with
multiscale wavelet analysis for marine applications: preserving spatial
information and minimizing artifacts (PSIMA)," Can. J. Remote
Sensing, 29(6):14-23, November 2003.
[18] S. T. Smith, "MATLAB advanced GUI development," Dog Ear
Publishing, 2006.
[19] O. Rockinger, "Various Registered Images," Available Online,
URL:http://www.imagefusion.org/, 2005
[1] M. Sasikala and N. Kumaravel, "A comparative analysis of featurebased
image fusion methods," Information Technology Journal,
6(8):1224- 1230, 2007.
[2] J. Daugman and C. Downing, "Gabor wavelets for statistical pattern
recognition," The handbook of brain theory and neural networks, M. A.
Arbib, ed. Cambridge, MA, USA: MIT Press, 1998, pp.414-420.
[3] S. Mallat, "Wavelets for a vision," Proceedings of the IEEE, New York
Univ., NY, 84(4):604-614, April 1996.
[4] A. Wang, H. Sun and Y. Guan, "The application of wavelet transform to
multimodality medical image fusion," Proc. IEEE International
Conference on Networking, Sensing and Control (ICNSC), Ft.
Lauderdale, Florida, 2006, pp.270-274.
[5] O. Rockinger, "Pixel-level fusion of image sequences using wavelet
frames," Proc. of the 16th Leeds Applied Shape Research Workshop,
Leeds University Press, 1996, 149-154.
[6] H. Li, B. S. Manjunath, and S. K. Mitra, "Multisensor image fusion
using the wavelet transform," Graphical Models and Image Processing,
57(3):235-245, May 1995.
[7] M. Jian, J. Dong and Y. Zhang, "Image fusion based on wavelet
transform," Proc., 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Distributed
Computing,,Qingdao, China, July 2007.
[8] Z. Yingjie and G. Liling, "Region-based image fusion approach using
iterative algorithm," Proc. Seventh IEEE/ACIS International Conference
on Computer and Information Science(ICIS), Oregon, USA, May 2008.
[9] H. Zhang, L. Liu and N. Lin, "A novel wavelet medical image fusion
method," International Conference on Multimedia and Ubiquitous
Engineering (MUE-07), Seoul, Korea, April 2007.
[10] V. Petrovic, "Multilevel image fusion," Proceedings of SPIE, 5099:87-
96, 2003.
[11] Y. Zheng, X. Hou, T. Bian and Z. Qin, "Effective image fusion rules of
multiscale image decomposition," Proc. of 5th International Symposium
on Image and Signal Processing and Analysis (ISPA07), Istanbul,
Turkey, September 2007, pp. 362-366.
[12] J. Gao, Z. Liu and T. Ren, "A new image fusion scheme based on
wavelet transform," Proc., 3rd International Conference on Innovative
Computing,Information and Control, Dalian, China, June 2008.
[13] I. Daubechies, "The wavelet transform, time-frequency localization and
signal analysis," IEEE Trans. Info. Theory, 36:961-1005, 1990.
[14] M. Vetterli and C.Herley, "Wavelets and filter banks: theory and
design," IEEE Transactions on Signal Processing, 40(9):2207-2232,
September 1992.
[15] S. G. Mallat, "A Theory for multiresolution signal decomposition - the
wavelet representation," IEEE Transactions on Pattern Analysis and
Machine Intelligence, 11(7):674-693, July 1989.
[16] R. C. Luo and M. G. Kay, "Data fusion and sensor integration: state of
the art 1990s," Data Fusion in Robotics and Machine Intelligence, M. A.
Abidi and R. C. Gonzalez eds., Academic Press, San Diego, 1992, pp.7-
135.
[17] Y. Du, P. W. Vachon, and J. J. V. Sanden, "Satellite image fusion with
multiscale wavelet analysis for marine applications: preserving spatial
information and minimizing artifacts (PSIMA)," Can. J. Remote
Sensing, 29(6):14-23, November 2003.
[18] S. T. Smith, "MATLAB advanced GUI development," Dog Ear
Publishing, 2006.
[19] O. Rockinger, "Various Registered Images," Available Online,
URL:http://www.imagefusion.org/, 2005
@article{"International Journal of Information, Control and Computer Sciences:55388", author = "Susmitha Vekkot and Pancham Shukla", title = "A Novel Architecture for Wavelet based Image Fusion", abstract = "In this paper, we focus on the fusion of images from
different sources using multiresolution wavelet transforms. Based on
reviews of popular image fusion techniques used in data analysis,
different pixel and energy based methods are experimented. A novel
architecture with a hybrid algorithm is proposed which applies pixel
based maximum selection rule to low frequency approximations and
filter mask based fusion to high frequency details of wavelet
decomposition. The key feature of hybrid architecture is the
combination of advantages of pixel and region based fusion in a
single image which can help the development of sophisticated
algorithms enhancing the edges and structural details. A Graphical
User Interface is developed for image fusion to make the research
outcomes available to the end user. To utilize GUI capabilities for
medical, industrial and commercial activities without MATLAB
installation, a standalone executable application is also developed
using Matlab Compiler Runtime.", keywords = "Filter mask, GUI, hybrid architecture, image fusion,Matlab Compiler Runtime, wavelet transform.", volume = "3", number = "9", pages = "2244-6", }