Image Fusion Based Wavelet Transform

version 1.0.0.0 (74.3 KB) by Abbas Hussien Miry
perform the image fusion using wavelet transform with max and average rules

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Updated 11 Apr 2016

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The algorithm of image fusion using DWT described in the following steps
1. Size of inputs images:
Given a two dimensional images (example, image A, image B) it is necessary to convert it into the same size a power of two square forms.
2. Computation of two dimensions DWT:
In this step, the two dimensional Discrete Wavelet Transform should be applied to the resized two dimensional images.
3. Fusion rule:
The most used of image fusion rule using wavelet transform is maximum selection, compare the two coefficients of DWT of the two images and select the maximum between. While the lowpass subband is an approximation of the input image, the three detail subbands convey information about the detail parts in horizontal, vertical and diagonal directions. Different merging procedures will be applied to approximation and detail subbands. Lowpass subband will be merged using simple averaging operations since they both contain approximations of the source images.
4. Inverse discrete wavelet transforms:
After selected the fused low frequency and high frequency bands, fused coefficient is reconstructed using the Inverse fast discrete wavelet transform to get the fused image which represent the new image.

Cite As

Abbas Hussien Miry (2022). Image Fusion Based Wavelet Transform (https://www.mathworks.com/matlabcentral/fileexchange/56494-image-fusion-based-wavelet-transform), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2009b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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