ISSN 2203-2843 30 | Page Australian Journal of Information Technology and Communication Volume II Issue I Optimal Image Fusion using Neuro-Fuzzy Algorithm and SVM Ms Maninder Kaur 1 , Ms Pooja 2 (Department of CSE,CTIEMT,CT Institution Shahpur,Jalandhar) maninder.khinda43@gmail.com 1 (Department of CSE, CTIEMT, CT Institution Shahpur,Jalandhar) poojachoudhary80@gmail.com 2 Abstract: This thesis presents a novel image fusion method which is suitable for pan-sharpening of multispectral (MS) bands, based on multi-resolution analysis. The low-resolution MS bands are sharpened by injecting high-pass directional details extracted from the high-resolution panchromatic (Pan) image by means of the Wavelet and curvelet transform which is a non-separable MRA whose basis function are directional edges with progressively increasing resolution. The fusion of high-spectral but low spatial resolution multispectral and low-spectral but high spatial resolution panchromatic satellite images is a very useful technique in various applications of remote sensing. Some studies showed that wavelet- based image fusion method provides high quality of the spectral content of the fused image. In this paper we introduce a new method based on the Wavelet and curvelet transform using Neuro-Fuzzy which represents edges better. Since edges play a fundamental role in image understanding one good way to enhance spatial resolution is to enhance the edges. Wavelet and curvelet-based image fusion method provides richer information in the spatial and spectral domains simultaneously. It will perform image fusion using Wavelet and curvelet Transform with Neuro-Fuzzy Algorithm. This new method has reached an optimum fusion result. For the implementation of this proposed work we use the Image Processing Toolbox under Matlab Software. Keywords: Image Fusion, Curvelet Transform, Wavelet Transform, Neuro-Fuzzy, SVM, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE). INTRODUCTION The process of including complementary and redundant information from different images into one composite image which includes a better description of the underlying scene is known as image fusion and these results in a fused image more useful for human visual and machine processing. Image fusion strategies are basically classified into pixel level and region level approaches. Pixel level techniques: The set of pixels in the source image determine each pixel in the fused image. Basically pixel level techniques are classified into spatial domain and transform domain techniques. Region level techniques: In this technique the segmentation of the images into regions and then based upon the extracted region fusion is performed. The process of image fusion combines two or more images. Different images contain different information is the main idea behind image fusion. Wavelet transforms is that in which the transformation should allow only changes in time extension, but not shape. This is affected by choosing suitable basis functions that allow for these Changes in the time. Curvelet are an appropriate basis for representing images which is smooth apart from singularities along smooth curves where the curves have bounded curvature where objects have a minimum length scale in the image. This property holds for cartoons and geometrical diagrams and text. WAVELET TRANSFORM: Wavelet transforms is that in which the transformation should allow only changes in time extension, but not shape. This is affected by choosing suitable basis functions that allow for these Changes in the time. A signal analysis method similar to image pyramids is the discrete wavelet transform. The main difference is that while image pyramids lead to an over complete set of transform coefficients, the wavelet transform results in a no redundant image. The discrete 2-dim wavelet transform is computed by the recursive application of low pass and high pass filters in each direction of the input image followed by sub sampling. One major drawback of the wavelet transform when applied to image fusion is its well known shift dependency this results in inconsistent fused images when invoked in image sequence fusion. A wavelet transform array is synthesized for the product image and populated from the source images based on a set of predefined rules. After population, this synthetic array is inverse wavelet transformed to create the product image. Graham provides a more detailed discussion of the application of wavelet theory to image