摘要
提出了一种基于粒子群优化算法的BP神经网络图像复原方法。BP神经网络具有很强的学习和泛化能力,可避免传统复原方法对先验知识的依赖性,粒子群算法的全局寻优能力弥补了BP算法对初始权值敏感、收敛速度慢和易陷入局部极小值等问题,将两者结合形成PSO-BP算法,使得图像复原的难度大大下降。实验表明,该方法对模糊图像的复原性能很好,收敛速度快,在视觉和定量分析上都获得了较好的效果。
A new method is proposed for image restoration of BP neural network based on particle swarm optimization. Different from traditional restoration methods,BP neural network has a strong learning and generalization ability to avoid the dependence on a priori knowledge.BP algorithm is sensitive to the initial weights,has slow convergence and is easy to fall into local minima,however,the PSO algorithm,as a global optimization algorithm,can make up for this issues and others.The two algorithms are combined to form a PSO-BP algorithm,making the difficulty of image restoration decline significantly. The experimental results show that the fuzzy image restoration method has better performance,fast convergence and better effects obtained in the visual and quantitative analysis.
出处
《无线电工程》
2014年第10期5-7,26,共4页
Radio Engineering
基金
国家自然科学基金资助项目(61175120)
关键词
图像复原
退化图像
BP神经网络
粒子群优化算法
mage restoration
image degradation
BP neural network
particle swarm optimization
