Matlab Code for Image Fusion using PCA

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PCA transformation is a statistical method. It transforms a group of related variables into a group of the original?variables. The aim is to compress multi-band image information into an image and information can perform maximum in?the new image. During the fusion process, it first carries on PCA transformation so that the gray scale mean and variance?are consistent with PCA component of the image.

PCA is the simplest true eigenvector-based multivariate analysis. It involves ways for identifying and to show patterns in?data, in such a way as to highlight their similarities and differences, and thus reduce dimension without loss of data. In?this method first the column vectors are extracted, from respective input image matrices. The covariance matrix is?calculated. Diagonal elements of covariance vector will contain variance of each column vector. The Eigen values and?the vectors of covariance matrix are calculated. ?Normalize column vector corresponding to larger Eigen value by dividing each element with mean of Eigen vector.?Those normalized Eigen vector values act as the weight values and are multiplied with each pixel of input image. Sum of?the two scaled matrices are calculated and it will be the fused image matrix.
The information flow diagram of PCA-based image fusion algorithm is shown in figure 4. The input images (images to?be fused) I1(x, y) and I2(x, y) are arranged in two column vectors and their empirical means are subtracted. The resulting?vector has a dimension of n x 2, where n is length of the each image vector. Compute the eigenvector and eigen?values for this resulting vector are computed and the eigenvectors corresponding to the larger eigen value obtained

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