| 基于PSO和改进神经网络的图像滤波方法的研究 |
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| 引用本文： | 张银雪,贾振红,蒋海军. 基于PSO和改进神经网络的图像滤波方法的研究[J]. 激光杂志, 2009, 30(4): 34-36 |
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| 作者姓名： | 张银雪 贾振红 蒋海军 |
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| 作者单位： | 张银雪,贾振红(新疆大学信息科学与工程学院,新疆,乌鲁木齐,830046);蒋海军(新疆大学数学与系统科学学院,新疆,乌鲁木齐,830046) |
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| 基金项目： | 教育部新世纪优秀人才支持计划项目 |
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| 摘 要： | 提出了一种基于改进BP神经网络和粒子群优化算法（PSO）的图像滤波方法。该方法利用对数最小均方误差函数（LNLS）代替BP神经网络传统的最小均方误差函数（LMS），用来减小图像噪声对神经网络精度的影响；并将改进后的BP神经网络利用PSO算法优化，从而避免神经网络陷入局部极小值点，进一步提高神经网络滤波能力。实验结果表明，与传统滤波方法相比，该方法不仅能有效地滤除图像中的高斯噪声而且能很好地保护图像细节。
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| 关 键 词： | 图像滤波 BP神经网络 对数最小均方误差 粒子群优化算法 |
Image filtering based on PSO and modified neural network |
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| Affiliation: | ZHANG Yin - xue, JIA Zhen - hong, JIANG Hai - jin ( 1 .College of Information Science ＆ Engineering, Xinjiang University, Urumqi 830046,China; 2. College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China ) |
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| Abstract: | A new method for image noise reduction based on Particle Swarm Optimization （PSO） and modified BP neural network is proposed in this paper. This method introduces BP neural network by utilizing least mean log squares （LMLS） error function instead of least mean squares （LMS） error function as its cost function, and then the modified BP neural network optimized with PSO. The proposed method can minish the influence on the accuracy of BP neural network model which controlled by image noise and avoid local minimum obviously. Experimental results demonstrate that the proposed new method can reduce Gaussian noise of images and preserve image details more effectively than traditional algorithms. |
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| Keywords: | image filtering BP neural network hyperbolic tangent error function Particle Swarm Optimization |
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