Essay --

Where where hR, hF are the normalized white-haired(a) level histograms of xRand xF, respectively. The joint gray level histogram ofxR and xF is denoted by hR,F, and L is the chip of bins. xF and xR correspond to the consolidated and reference forms, respectively. I(xRxF) indicates how much information the coalesced fancy xF conveys about the reference xR. Thus, higher the mutual information between xF and xR, on that point are more chances that xF resembles the ideal xR.D. Entropy (EN)-Entropy stinkpot be used to measure the difference between 2 source estimates and the fused image. The entropy of an image is a measure of information field of study. Entropy is the reasonable number of bits which have a need of quantize the intensities in the image. It is be 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 mor e information than the original image sources.V. PROPOSED SOFTWARE DESIGN syner lead offic software is developed to do the reliable monitoring and management of coalition 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.CONCLUSIONWith 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 coalition 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 An Overview on Pixel-Level I mage Fusion in irrelevant Sensing, Proceedings of the IEEE International Conferenc e on Automation and Logistic,vol 6, no .4, pp .2339- 2344 feb (2007)13)image code, IEEE proceedings on Communications, vol. 31, pp. 532540, 1983.14) R. H. Bamberger and M. J. T. Smith, A filter bank for the directional decomposition of images scheme and design, IEEE Transactions on Signal Processing, vol. 40, no. 4, pp. 882893, 1992.15) G. H. Qu, D. L. Zhang, and P. F. Yan, Information measure for accomplishment of image fusion, Electronic Letters, vol. 38, no. 7, pp.313315, 2002.16) H. Tian, Y.-N. Fu, and P.-G. Wang, Image fusion algorithm found on regional variance and multi-wavelet bases, in Proc. of 2nd Int. Conf. Future computing machine and Communication, vol. 2, 2010, pp