Image Processing Reference

In-Depth Information

all these bands are clubbed into a single color channel. Each of the groups have been

fused separately, and the three fused images are assigned to R, G, and B channels to

form a composite color output.

7.6 Summary

This chapter develops a fusion methodology that transforms the problem into an

optimization framework where visualization is the primary aim. Therefore, it focuses

on computing the fusion weights optimally in order to provide better visualization

results. The novelty of the discussed solution lies in the fact that the fusion process

is driven by the desired properties of the output image as opposed to the traditional

methods which are driven by the input. We have explained a multi-objective cost

function based on some of these image properties, and then discussed the solution

using the Euler-Lagrange equation. Being completely output-driven, this solution

does not require any assumption on how the weights should be related to the input

images.

Our discussion also includes all typical constraints of pixel-based image fusion.

We have, thus, demonstrated how fusion can be represented as an unconstrained

optimization problem by introducing an auxiliary variable which leads to a compu-

tationally simpler and easier solution.