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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 3    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 3,March 2012
Fusion Technique for Multi-focused Images using Stationary Wavelet Packet Transform
Full Text(PDF, )  PP.569-576  
Dr. A. A. Gurjar, Ms. Swapna M. Patil, Prof. S. B. Kasturiwala
Image Fusion, Multi Wavelets, Stationary Wavelets, Wavelet Packets, Peak Signal to Noise ratio, Root Mean Square Error, Quality Index and Normalized Weighted Performance Metric
`Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Transform fusion uses transform for representing the source images at multi scale. The most common widely used transform for image fusion at multi scale is Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance & poor directionality and Stationary Wavelet Transform and Wavelet Packet Transform overcome these disadvantages. The Multi-Wavelet Transform of image signals produces a non-redundant image representation, which provides better spatial and spectral localization of image formation than discrete wavelet transform. In this paper, Multi-Wavelet Transform, Stationary Wavelet Transform and Wavelet Packet Transform were combined to form Multi-Stationary Wavelet Packet Transform and its performance in fusion of multi-focused images in terms of Peak Signal to Noise Ratio, Root Mean Square Error, Quality Index and Normalized Weighted Performance Metric is presented.
1. S. Mallat, Wavelet Tour of Signal Processing, New York Press, 1998.

2. Rick S. Blum and Yang Jin zhong, 2006, Image Fusion Methods and Apparatus, US Patent, WO/2006/017233.

3. C.S. Xydeas ―Objective Image Fusion Performance Measure‖, Electronics Letter, Vol.36, N0.4, pp. 308-309, 2000.

4. Zhou Wang ―A Universal Image Quality Index‖, IEEE Signal Processing Letters, Vol. 9, No.3, pp. 81-84, March, 2002.

5. S. Mallat, A theory for Multiresolution signal decomposition: The wavelet representation, IEEE transaction pattern anal. Machine Intell., vol. 11, no. 7, pp 674-693, July 1989.

6. N. G. Kingsbury, ‖Image processing with complex wavelets‖, Philos. Trans. R. Soc. London A, Math. Phys. Sci., 357(1760):2543–2560, September 1999.

7. N. G. Kingsbury, ―Complex wavelets for shift invariant analysis and filtering of signals‖‟ Journal of Appl. and Comp. Harmonic Analysis, 10(3):234– 253, May 2001.

8. I.W.Selesnick. ―Smooth wavelet tight frames with zero moments‖, Journal of Appl. Comput. Harmon. Anal. 2001, 10(2), 163-181.

9. N.G. Kingsbury, ―Complex wavelets for shift invariant analysis and filtering of signals‖ Applied Computational Harmonic analysis, vol. 10, no.3, pp 234-253, May 2001.

10. W. Selesnick, ―The design of approximate Hilbert transform pairs of wavelet bases‖, IEEE Trans. Signal Processing, 50(5):1144–1152, May 2002.

11. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, ―The dual-tree complex wavelet transforms - A coherent framework for Multiscale signal and image processing‖‟ IEEE Signal Processing Magazine, 22(6):123–151, November 2005.

12. R. Yu and H. Ozkaramanli, ―Hilbert transform pairs of orthogonal wavelet bases: Necessary and sufficient conditions‖ IEEE Trans. Signal Processing, 53(12):4723–4725, December 2005.

13. N.G. Kingsbury, ―The dual tree complex wavelet transform: A technique for shift invariance and directional filters‖ in proc. Of 8th IEEE DSP Workshop, Utah, paper no. 86 August 9-12, 1998.

14. Zhu Shu-long, Image Fusion using Wavelet Transform‖, Symposium on Geospatial Theory, Processing and Applications, pp. 5-9, 2004.

15. P. J. Burt and R. J. Kolczynski, ―Enhanced image capture through image fusion‖, proceedings of the 4th International Conference on Computer Vision, pp. 173-182, 1993.

16. H. Li, B.S. Manjunath, and S.K. Mitra, ―Multi-sensor image fusion using the wavelet transform‖, Proceedings of the conference on „Graphical Models and Image Processing‟ , pp. 235–245, 1995.

17. Gonzalo Pajares and Jesus Manuel de la Cruz, ‖ A Wavelet based Image fusion – Tutorial‖, Pattern Recognition 37(2004),1855-1872.

18. P.J. Burt , ―The Laplcian Pyramid as a Image Codec‖, IEEE Transactions on Communications, Vol. No.4, pp. 532-540

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