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图学学报

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基于改进粒子群算法的图像灰度增强研究

  

  • 出版日期:2013-12-31 发布日期:2015-06-19

Image Gray Enhancement Based on Improved Particle Swarm Optimization Algorithm

  • Online:2013-12-31 Published:2015-06-19

摘要: :针对灰度图像增强的特点,采用具有混沌量子特性的粒子群优化算法。首
先粒子以全局最优解更新自身的速度和位置;接着量子效应的概率密度函数使束缚状态的粒
子以一定概率出现在整个可行搜索空间的任何位置;然后混沌状态使粒子从无序到有序转
变,相关因子避免了搜索的盲目性;最后灰度图像采用非线性映射曲线变换,其函数转化为
改进粒子群算法的参数。实验仿真显示算法对图像灰度增强效果优,定量评价指标好,时效
性佳。

关键词: 混沌, 量子, 灰度增强

Abstract: Aiming at grayscale image enhancement features, the quantum properties of
chaotic particle swarm optimization studies are used. First, the global optimal solution particles
update their velocity and position; then the probability density function of quantum effects makes
the particle in bound state to appear, with a certain probability, anywhere in the entire feasible
search space ; the chaotic state makes the particles change from disorder to order , with the related
factors avoiding searching blindness; final grayscale image uses nonlinear mapping curve
transformation, and its function is translated into parameters of improved PSO algorithm. The
simulation shows the algorithm excellent for image grayscale enhancement better in quantitative
evaluation and good in time efficiency.

Key words: chaos, quantum, gray enhancement