ISSN: 2250-3005 - ijcer

International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 86. ConclusionThis research proposes a method to remove the motion blur present in the image taken from any cameras. Theblurred image is restored using Blind de-convolution method with N=20 number of iteration and DWT using averaging,maximum likelihood and window based method. The result based on deconvolution does not improve the image qualitydrastically. If we compare the rmse of blurred image and fused image we can see that fused image rmse is higher than blurredimage rmse. It is also proved by performing visual comparison among all the fused image. There is still significantdifference between blurred image and fused image of fusion using pixel average and maximum likelihood approach. Medicalimage rmse is significantly higher than blurred image in all the methods except window based method. The primitive fusionschemes like pixel averaging and maximum pixel perform the fusion right on the source images. These methods often haveserious side effects such as reducing the contrast of the image as a whole. But these methods do prove good for certainparticular cases wherein the input images have an overall high brightness and high contrast. Further window based methodcompared for fusion, and it gave the best results. If computationally it‟s performance is compared it‟s rmse of fused imagefor all type of image is minimum, also the fused image quality is improved because the fused image rmse is lower thanblurred image rmse. We can do further satisfaction by visual comparison of all the fused images. The challenge is to design amethod that exhibits the most appropriate compromise among computational complexity, reliability, robustness to noise, andportability for a given application.7. References[1] Hui Li; Manjunath, B.S.; Mitra, S.K” Multi sensor image fusion using the wavelettransform” Image Processing, 1994.Proceedings. ICIP-94., IEEE International Conference, 1994 , Page(s): 51 - 55 vol.1 .[2] Implementation and Comparative Study of Image Fusion Algorithms “ International Journal of Computer Applications(0975 – 8887)Volume 9– No.2, November 2010”[3] Gihong Qu, Dali Zhang and Pingfan Yan, "Medical image fusion by wavelet transfo rm modulus maxima," OpticsExpress, vol. 9, No. 4 pp.184-190,Aug. 2001[4] S.Mallat “An Improved Image Denoising Method Based on Wavelet Thresholding”journal of signal and informationprocessing PP.109-116 DOI: 10.4236/jsip.2012.31014[5] Zhu Shu-Long, "Image fusion using wavelet transform,”Symposium on Geospatial Theory, Processing and Applications,Ottawa 2002.[6] Hongbo Wu; Yanqiu Xing “Pixel-based Image Fusion Using Wavelet Transform for SPOT and ETM+ Image”Publication Year: 2010 , Page(s): 936 - 940[7] Y. Xia, and M. S. Kamel “Novel Cooperative Neural fusion Algorithms for Image Restoration, Image Fusion”, Feb2007.||Issn 2250-3005(online) || ||December| 2012 Page 338