Introduction: multiple ways to get new coefficients so that

Introduction: image fusion can be differentiated into three categories Simple image fusion, pyramid image fusion, and Discrete wavelet transform.The primitive fusion like minimum/maximum and PCA do the fusion on the source image.Due to which the contrast gets reduced.Pyramid transform works as by taking the pyramid transform of fuse image from the source image.And finally obtained the transformed image by taking the inverse pyramid transform.But in pyramid transform blocking effect is there due to the failure of spatial orientation selectivity.Now In-depth study found that the dwt has orthogonality, direction selectiveness & compactness.DWT is multi-resolution technique I.e. capacity to decompose functions into various scale level. Discrete Wavelet Transform: DWT can supply improved spatial and spectral localization of image data as compared to other multiresolution content.Wavelet supported schemes execute better in terms of color distortion.So, DWT is mostly used for remote sensing, medically & in multi-focusing.Some of the discrete wavelets are Haar wavelet. The advantage of Discrete Wavelet Transform: Different image resolution can be managed by DWT.DWT allows image decomposition in various kinds of coefficients conserving the image information. These coefficients from multiple images can be joint in multiple ways to get new coefficients so that the information in the original images is gathered suitably.l The inverse wavelet transform is applied to return back the ultimate fused image, where the necessary applicable information from the source images is conserved in the fused image.l Most of the building of DWT make use of the multiresolution analysis, which precise it by a scaling function. A subsidiary or scaling function confront the numerical complexity of evaluating an integral of each wavelet coefficient in DWT. Working on Discrete Wavelet Transform: Image is decomposed into four frequency band by two-dimensional wavelet transform: low-low(LL), low-high(LH), high-low(HL) and high-high(HH). Amid these four, the image is decomposed into LH, HL and HH spatial frequency bands at various scales and the LL band at the harsh scale. LL band comprise average image information whereas some other bands comprise directional info due to spatial orientation. Higher absolute values of wavelet coefficients in the high bands correspond to the outstanding characteristic such as edges, lines, etc. DWT based image fusion: For fusing low-resolution Multispectral (MS) image with a high-resolution Panchromatic (PAN) image using wavelet fusion technique, the PAN image is first decomposed into a set of low-resolution PAN images with corresponding wavelet coefficients (spatial details) for each level. An individual band of MS image replaces the low-resolution PAN at the resolution level of the original MS image. The high-resolution spatial detail is injected into each MS band by performing inverse wavelet transform on each MS band together with the corresponding wavelet coefficients. Generally, in wavelet-based fusion schemes, detailed information is extracted from the PAN image using wavelet transforms and injected into MS image.  undefined