International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8 ||Issn 2250-3005(online) || ||December | 2012 Page 332 Motion Blur Image Fusion Using Discrete Wavelate Transformation Er. Shabina Sayed Department Of Information Technology, MHSS COE, Mumbai, India Abstract The methodology for implementing a image fusion system using deconvolution and discrete wavelate transformation is proposed in this papers. This project proposes a method to remove the motion blur present in the image taken from any cameras. The blurred 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 comparision result of both the method prove that image restoration using dwt gives better result than image restoration using deconvolution. Ke ywor ds : multisensory system,pyramid transform,discrete wavelet transform,Motion blur,blind deconvolution , 1. Introduction With the recent rapid developments in the field of sensing technologies multisensory systems[1,2] have become a reality in a growing number of fields such as remote sensing, medical imaging, machine vision and the military applications for which they were first developed. The result of the use of these techniques is a great increase of the amount of data available. Image fusion provides an effective way of reducing this increasing volume of information while at the same time extracting all the useful information from the source images. Multi-sensor images often have different geometric representations, which have to be transformed to a common representation for fusion. This representation should retain the best resolution of either sensor. A prerequisite for successful in image fusion is the alignment of multi-sensor images. Multi- sensor registration is also affected by the differences in the sensor images.However, image fusion does not necessarily imply multi-sensor sources, there are interesting applications for both single-sensor and multi-sensor image fusion, as it will be shown in this paper.The primitive fusion schemes perform the fusion right on the source images.One of the simplest of these image fusion methods just takes the pixel-by-pixel gray level average of the source images. This simplistic approach often has serious side effects such as reducing the contrast. With the introduction of pyramid transform in mid-80's[3], some sophisticated approaches began to emerge. People found that it would be better to perform the fusion in the transform domain. Pyramid transform appears to be very useful for this purpose. The basic idea is to construct the pyramid transform of the fused image from the pyramid transforms of the source images, and then the fused image is obtained by taking inverse pyramid transform. Here are some major advantages of pyramid transform:  It can provide information on the sharp contrast changes, and human visual system is especially sensitive to these sharp contrast changes.  It can provide both spatial and frequency domain localization There are many transformations which can be used but Basically this paper makes the contribution of the two important transformation . 2. Discrete Wavelet Transformation(DWT) The wavelet transform[4,7], originally developed in the mid 80‟s, is a signal analysis tool that provides a multi - resolution decomposition of an image in a bi orthogonal basis and results in a non-redundant image representation. These bases are called wavelets, and they are functions generated from one single function, called mother wavelet, by dilations and translations. Although this is not a new idea, what makes this transformation more suitable than other transformations such as the Fourier Transform or the Discrete Cosine Transform, is the ability of representing signal features in both time and frequency domain.Fig.1 shows an implementation of the discrete wavelet transform. In this filter bank, the input signal goes through two one-dimensional digital filters. One of them, H 0 , performs a high pass filtering operation and the other H 1 a low pass one. Each filtering operation is followed by sub sampling by a factor of 2. Then, the signal is reconstructed by first up sampling, then filtering and summing the sub bands. Figure 1.two channel filter bank