help me in the code for image fusion using wavelet based principle component analysis

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There is function in the Wavelet Toolbox for image fusion, wfusimg, and a demo, imagefusiondemo.m, but it does not use a wavelet-based PCA.
There also is a wavelet PCA routine, wmspca, but that is designed for multisignal analysis, i.e. each column vector is a 1-D signal and not on images.

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wmpca is for multiscale PCA. but i want wavelet based PCA.
Hi, i am trying to implement wmspca inbuilt function step by step and compare it with eigen vectors in Wavelet Analyzer app also. when i input a 1024 x 4 signal matrix and apply 5 level dwt on each column. If i choose 1 principal component how can i reconstruct using inverse dwt as wavelet coefficients length and eigen vector lengths are totally different?.
Any help is appreciated. Thanks in advance.

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