Abstract
Fusion is basically extraction of best of inputs and conveying it to the output. In this paper, we present an image fusion technique using the concept of perceptual information across the bands. This algorithm is relevant to visual sensitivity and tested by merging multisensor, multispectral and defoucused images. Fusion is achieved through the formation of one fused pyramid using the DWT coefficients from the decomposed pyramids of the source images. The fused image is obtained through conventional discrete wavelet transform (DWT) reconstruction process. Results obtained using the proposed method show a significant reduction of distortion artifacts and a large preservation of spectral information.
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References
Akerman, A.: Pyramid techniques for multisensor fusion. In: Proc. SPIE, vol. 1828, pp. 124–131 (1992)
Brown, L.: A survey of image grgistration techniques. ACM Computing Survey 24, 325–376 (1992)
Burt, P., Kolczynski, R.: Enhanced image capture through fusion. In: Proc. 4th Int. Conf. Computer Vision, Berlin, Germany, pp. 173–182 (1993)
Burt, P.: The pyramid as structure for efficient computation. In: Multiresolution Image Processing and Analysis, pp. 6–35. Springer, New York (1984)
Chipman, L., Orr, T., Graham, L.: Wavelets and image fusion. In: Proc. of SPIE, vol. 2569, pp. 208–219 (1995)
Chen, H.M., lee, S., Rao, R.M., Slamani, M.A., Varshney, P.K.: Imaging for Concealed Weapon Detection. IEEE Signal Processing Magazine, 52–61 (March 2005)
Elsen, E., Pol, E., Viergever, E.: Medical image matching - A review with classification. IEEE Eng. Med. Biol., 26–39 (March 1993)
Escamilla, P.J., Mort, N.: Hybrid Kalman Filtr-Fuzzy Logic Adaptive Multi-sensor Data Fusion Architectures. In: Proceedings of the 42nd IEEE Conference on Decision and Contral, pp. 5215–5220 (December 2003)
Escamilla, P.J., Mort, N.: A hybrid Kalman filter-Fuzzy logic architecture for multisensor data fusion. In: Proceedings of the 2001 IEEE International Symposium on Intelligent contral, September 5-7, pp. 364–369 (2001)
Qu, G., Zung, D., Yan, P.: Information measure for performance of image fusion. Electronic letters, 313–315 (March 2002)
Li, H., Munjanath, B., Mitra, S.K.: Contour based multisensor image registration. In: Proceedings 26 thAsilomar Conference on Signal, Systems and Computers, Pacific Grove, CA, November 1992, pp. 182–186 (1992)
Li, H., Munjanath, B., Mitra, S.K.: A contour based approach to multisensor image registration. IEEE Trans. Image Processing 4(3), 320–334 (1995)
Li, H., Munjanath, B., Mitra, S.K.: Registration of 3-D brain images by curve mathcing. In: Proceedings IEEE medical imaging Conference, San Francisco, CA, November 1993, pp. 1744–1748 (1993)
Li, H., Munjanath, B., Mitra, S.K.: Multisensor image fusion using the wavelet transform. Graph. Models Image Process. 57(3), 235–245 (1995)
Liu, X., Yang, W.: Enhanced visualization of images through fusion. In: Proc. SPIE, October 2000, vol. 4231, pp. 340–345 (2000)
Luo, R., Kay, M.: Data fusion and sensor intergration: state of the art in 1990s. In: Abidi, M., Gonzalez, R. (eds.) Data Fusion in Robotics and Machine Intelligence, pp. 7–136. Academic Press, San Diego (1992)
Mallat, S.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Machine Intell. 11, 674–693 (1989)
Mort, M., Prajitno, P.J.: A Multisensor Data fusion -based target tracking system. In: IEEE ICIT-2002, Thiland, pp. 427–432 (2002)
Petrovic, V., Xydeas, C.: Gradient based Multiresolution Image Fusion. IEEE Transaction on Image Processing 13(2) (February 2004)
Petrovic, V., Xydeas, C.: Objective Pixel-level Image fusion Performance Measure. In: Proceedings of SPIE, vol. 4051, pp. 89–98 (2000)
Petrovic, V., Xydeas, C.: Multiresolution image fusion using cross band feature selection. In: Proc. SPIE, April 1999, vol. 3719, pp. 319–326 (1999)
Toet, A., Ruyven, L.V., Velaton, J.: Merging thermal and visual images by a contrast pyramid. Opt. Eng. 28(7), 789–792 (1989)
Toet, A.: Hierarchical image fusion. Mach. Vis. Appl. 3, 3–11 (1990)
Uner, M.K., Ramac, L., Varshney, P., Alford, M.: Concealed Weapon Detection: An image fusion approach. In: Proc. of SPIE, vol. 2942, pp. 123–132 (1997)
Zhang, Z., Blum, R.: A categorization of multiscale-decomposition based image fusion schemes with a performance study of a digital camera application. Proc. IEEE 87, 1315–1326 (1999)
Wang, Z., Alan Bovik, C.: An Universal image quality index. IEEE Signal Processing Letters 9(3) (March 2002)
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Shahid, M., Gupta, S. (2005). Image Merging Based on Perceptual Information. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_75
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DOI: https://doi.org/10.1007/11552499_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
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