ABSTRACT: Image fusion is processes of combining complementary information from a set of input images. The resultant fused image give large and reliable information. In this paper we study about Discrete wavelet and Discrete Multiwavelet and there use in image fusion. Discrete wavelet transform (DWT) technique is used for multi Resolution fusion. Multi Resolution fusion uses wavelet transform at multi scale for the representation of the source images. Multiwavelets are extension of scalar wavelets, and have many advantages over scalar wavelets. Multiwavelet analysis can provide a more absolute image analysis than wavelet multiresolution analysis.In this paper DWT and DMWT are qualitatively compaired with each other Keywords: Image fusion, multiwavelet transform,wavelet transform . multisensor image . I.INTRODUCTION Image Fusion is defined as the task or technique of combining two or more images into a single image. The new single image retains important information from each input image. Image fusion is a powerful tool used to increase the quality of image. Image fusion increases reliability, decreases uncertainty and storage cost by a single informative image than storing multiple images Image fusion can take place at three different levels pixel feature,decision level Image fusion technique can be classified into two categories -Direct Image Fusion and Multi resolution Image Fusion. Multiresolution Image fusion techniques based on pixel level fusion methods. Multi Resolution fusion uses wavelet and multiwavelet transform at multi scale for the representation of the input images. Image fusion based on the DWT can provide better performance than fusion based on other multiscale methods such as Laplacian pyramid, morphological pyramid. Wavelet transform in multiresolution can provide good localization in both frequency and space domains. In comparision with other multiscale transforms, the discretewavelet transform is more compact, and give detail about directional information in the low-low, high-low, low-high, and high-high bands, and contains unique information at different resolutions. The main dwarback of the scalar wavelet functions is time-frequency localization property. Multiwavelets have more than two scaling and wavelet functions. The multi-wavelet has many outstanding properties like orthogonality, short support, symmetry, and high degree of vanishing moments which is desirable for image processing . A multiwavelet system provides perfect reconstruction also preserve length (orthogonality), good performance at the boundaries , and a high order of approximation . By this multiwavelets gives superior performance for image processing applications as compared with the scalar wavelets. II.IMAGE FUSION: SCALAR WAVELETS The wavelet transform is use to detect local features in a signal process. It also used for decomposition of two dimensional (2D) signals such as 2D gray-scale image signals for multiresolution analysis. In wavelet transforms a signal is decomposed in lower frequency band and high frequency bands. In discrete wavelet transform (DWT), twochannel filter bank is used. When decomposition is performed, the approximation and detail component can be separated 2-D Discrete Wavelet Transformation (DWT) converts the image from the spatial domain to frequency domain. The image is divided by vertical and horizontal lines and represents the first-order of DWT, and the image can be separated with four parts those are LL1, LH1, HL1 and HH1. [10
