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Image Fusion Scheme based on Wavelet Transformation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 10 |
| Year of Publication: 2018 |
| Authors: Nisar Ahmed, Ruhiat Sultana, Syed Abdul Sattar |
10.5120/ijca2018916099
|
Nisar Ahmed, Ruhiat Sultana, Syed Abdul Sattar . Image Fusion Scheme based on Wavelet Transformation. International Journal of Computer Applications. 179, 10 ( Jan 2018), 16-20. DOI=10.5120/ijca2018916099
Abstract
The paper focuses on image fusion between multi-spectral images and panchromatic images using a gradient based wavelet analysis method with image processing traits. For highlighting the core features of source images, pre-processing is accomplished. A new gradient technique is developed based on wavelet transformation. As of gradient process, for a multi-spectral image, every pixel having 4 neighbors of each of them. The summation that we do matches those of the multi-spectral images that we have selected. Two major considerations for gradient are considered. One is for the pixel values in the multi-spectral images region and other is the gradient pixel values that are at the boundary. Once we have calculated these values, we transfer these values from the multi-spectral images onto the panchromatic image and then rest of the values outside the mask are kept as same. Since we are working on the RGB images, we copied the RGB channels directly. By calculating bigger gradient of the two images either in multi-spectral images or panchromatic images and then construct the final image using inverse wavelet transformation. The proposed method is experimented on satellite, medical and natural images. In all the cases, this method shown superiority with existing methods.
References
- C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” Proc. 6th Int. Conf. Computer vision. Washington, pp. 839–846, 1998.
- . A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in IEEE Computer Vision and Pattern Recogognition (CVPR), 2005, pp. 60–65.
- JIA Yong-hong，Li de-ren，SUN Jia-bing. Multi-dimensional remote sensing imagery data fusion. Remote sensing technology and application2000,15 (1):41-44
- LIU Xiao-long. Study on multi-resource remote sensing imagery information maintaining fusion technology. Journal of image and graphics, 2001, 6 (7):636-641.
- LI Jun，LIN Zong-jian. Remote sensing imagery data fusion method based on the characteristic. Journal of image and graphics, 1997, 2 (2):103-107.
- T. Westra, K. C. Mertens, and R. R. D. Wulf, “Wavelet-based fusion of SPOT/VEGETATION and ENVISAT/ASAR wide swath data for wetland mapping,” in SPOT/VEGETATION Users Conference, 2004, pp.24–26.
- Z. Korona and M. M. Kokar, “Multiresolution multisensory target identification,” In J. D. Irwin (Ed.), The Industrial Electronics Handbook, vol. 12, pp. 1627–1632, 1997.
- Z. Zhang and R. S. Blum, “A region-based image fusion scheme for concealed weapon detection,” in St Annual Conference on Information Sciences and Systems, 1997, pp. 168–173.
- L. Alparone, L. Facheris, S. Baronti, A. Garzelli, and F. Nencini, “Fusion of multispectral and sar images by intensity modulation,” in Int. Conf. Inf. Fusion, Stockholm, Sweden, 2004, pp. 637–643.
- W. Dou, Y. Chen, X. Li, and Z. Sui, “A general framework for component substitution image fusion: An implementation using the fast image fusion method,” Computational Geoscience, vol. 33, no. 2, pp. 219–228, 2007.
- M. Joshi, L. Bruzzone, and S. Chaudhuri, “A model-based approach to multiresolution fusion in remotely sensed images,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 9, pp. 2549–2562, 2006.
- W. Zhang and J. Kang, “Quickbird panchromatic and multi-spectral image fusion using wavelet packet transform,” Lecture Notes in Control and Information Sciences. Berlin, Germany: Springer-Verlag, vol. 344,pp. 976–981, 2006.
- V. Shah, N. Younan, and R. King, “An efficient pan-sharpening method via a combined adaptive pca approach and contourlets,” IEEE Trans. Geosci. Remote Sens., vol. 46, pp. 1323–1335, 2008.
- X. Otazu, M. Gonzlez-Audcana, O. Fors, and J. Nnez, “Introduction of sensor spectral response into image fusion methods,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 10, 2005.
- Y. Zhang, “A new merging method and its spectral and spatial effects,” International Journal of Remote Sensing, vol. 20, no. 10, pp. 2003–2014, 1999.
- Daubechies I.The wavelet transforms time–frequency localization and signal analysis. IEEE TransInfTheory1990; 36:961–1005.
- Mendlovic D, Konforti N. Optical realization of the wavelet transform for two-dimensional objects. ApplOpt1993; 32:6542–6.
- A. Alejaily, I. Eirule, and M. Mangoud, “Fusion of remote sensing images using contourlet transform,” in Intl. Conf. on Innovations and Advanced Technique in systems, Computing Sciences and Software Engineering, 2008, pp. 213–218.
- S. Raman and S. Chaudhuri, “Bilateral filter based compositing for variable exposure photography,” in Eurographics Conf., Munich, Germany, 2009.
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