Multi-source image fusion integrates multiple images derived from the same scene or target collected into a
new image to obtain more accurate and more complete description about the scene or target. The multi-objective optimization
problem of multi-source image fusion is researched in the transform domain. Based on the analysis of multiobjective
optimization theory and algorithms, an adaptive differential evolution algorithm is proposed. With adaptive variance
factor, dynamical crossover probability function and optimal elite ordering strategy, the algorithm reflects not only
good search capability but also good convergence. When applied to multi-objective optimization of multi-source image
fusion of transform domain, it will be an effective solution to the comprehensive evaluation in the image fusion process.