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This method extracts features with the highest mutual information value.
The mutual information value shows the similarity between the fusion image and the source image.
Simulation results will show that this upper bound is closed to the true mutual information value.
It can be observed that the closed-form solution and the numerical solution yield the same optimal mutual information value.
If the correlation of a pair of features is 0.6 or higher, we consider one feature in the pair to be redundant, and remove the feature with a lower mutual information value.
However, the more important observation is that both the end-to-end mutual information values of the wireless systems having the proposed precoding designs are larger than the mutual information value of the design in ([27], Section V-C).
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The mutual information values of the compressive sensing image fusion algorithm are the best among the three fusion methods.
However, these mutual information values are less than and closed to the upper bound of the mutual information obtained by the asymptotic solution.
In MRMR, the mean value of all mutual information values across individual features and class labels are calculated as the max-relevance values.
As can be seen from Figure 3, the mutual information values of the compressive sensing image fusion algorithm are the best among the three fusion methods.
The mutual information values reveal that there is synergy within the considered innovation systems over the period of study at all levels.
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