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中文摘要：
提出了一种基于量子行为的粒子群优化算法（QPSO）的图像融合方法。将图像融合问题归结为最优化问题，采用了QPSO算法进行优化。QPSO不仅参数个数少，其每一个迭代步的取样空间能覆盖整个解空间，因此能保证算法的全局收敛。与PSO算法和遗传算法进行了比较，证明了QPSO算法在图像融合中具有良好的效果。
英文摘要：
The paper proposed an image fusion approach based on QPSO algorithm. Formulated the image fusion problem as an optimization problem and adopt Quantum-behaved Particle Swarm Optimization algorithm to solve the problem. Not only QPSo has less parameters to control, but also does its sampling space at each iteration covers the whole solution space. Thus QPSO can find the best solution quickly and guarantee to be global convergent. Genetic Algorithm （GA） and Particle Swarm Optimization （PSO） were tested for performance comparison with QPSO, and the result showed the good efficiency of QPSO algorithms to image fusion.
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