基于量子行为微粒群优化算法的图像增强方法

Image enhancement based on quantum-behaved particle swarm optimization

为了提高图像增强的自适应性和通用性,提出了基于量子行为的微粒群优化算法(QPSO)的图像增强方法,将图像增强作为最优化问题来明确地表示。并且使用了一种新的目标函数评价算法的性能。QPSO没有过多参数需要调整,随机性强,能够保证算法的高效性和全局收敛性。实例仿真证实了QPSO在图像增强上的有效性和优越性。

To improve the adaptability and versatility of image enhancement, a Quantum-behaved Particle Swarm Optimization （QPSO） algorithm approach to image enhancement was proposed, in which image enhancement was formulated as an optimization problem. And a new objective function was used to evaluate the algorithm＇s performance. Not many parameters of QPSO need to be adjusted and the randomicity of QPSO is strong, so QPSO can guarantee the efficiency and global convergence of algorithm. The efficiency and superiority of image enhancement based on QPSO algorithms can be confirmed by the simulation results.