Essay --

Where; where hR, hF are the normalized gray level histograms of xR and xF, respectively. The joint gray level histogram of xR and xF is denoted by hR,F, and L is the number of bins. xF and xR correspond to the fused and reference images, respectively. I(xR;xF) indicates how much information the fused image xF conveys about the reference xR. Thus, higher the mutual information between xF and xR, there are more chances that xF resembles the ideal xR. D. Entropy (EN);- Entropy can be used to measure the difference between two source images and the fused image. The entropy of an image is a measure of information content. Entropy is the average number of bits which have a need of quantize the intensities in the image. It is represented as follows : where p(g) is the probability of grey-level g , and the range of g is [0,.....,L-1].High information content of image would have high entropy. High entropy of fused image indicates that the it contains more information than the original image sources. V. PROPOSED SOFTWARE DESIGN Interactive software is developed to do the reliable monitoring and management of Fusion process. The system software is made using MATLAB .We are taking two images image A and image B after the process of Counterlet transform. We get one output fused image. VI.CONCLUSION With this we conclude that contourlet Transform can be used to fuse two dimensional images and represent them more efficiently, which makes the fused images more clear and more informative. Contourlet Transform overcomes the drawbacks of traditional Image Fusion schemes by using ALM. The Experimental results using this technique of IF show that it can preserve more useful information in the fused image with higher spatial ... ....7, pp . 372-377( 2009) 12) Yi Yang ,Chongzhao Han ,Xin Kang and Deqiang Han â€Å"An Overview on Pixel-Level I mage Fusion in Remote Sensing,† Proceedings of the IEEE International Conference on Automation and Logistic,vol 6, no .4, pp .2339- 2344 feb (2007) 13)image code,† IEEE Transactions on Communications, vol. 31, pp. 532–540, 1983. 14) R. H. Bamberger and M. J. T. Smith, â€Å"A filter bank for the directional decomposition of images: theory and design,† IEEE Transactions on Signal Processing, vol. 40, no. 4, pp. 882–893, 1992. 15) G. H. Qu, D. L. Zhang, and P. F. Yan, â€Å"Information measure for performance of image fusion,† Electronic Letters, vol. 38, no. 7, pp. 313–315, 2002. 16) H. Tian, Y.-N. Fu, and P.-G. Wang, â€Å"Image fusion algorithm based on regional variance and multi-wavelet bases,† in Proc. of 2nd Int. Conf. Future Computer and Communication, vol. 2, 2010, pp