基于L＆A—PCNN模型的混合噪声滤除

华南理工大学学报（自然科学版） ›› 2009, Vol. 37 ›› Issue (4): 31-36.

基于L＆A—PCNN模型的混合噪声滤除

涂泳秋¹ 黎绍发¹ 王成¹ 王敏琴²

1. 华南理工大学计算机科学与工程学院, 广东广州 510640; 2. 肇庆学院计算机科学与软件学院, 广东肇庆 526061

收稿日期:2008-05-06 修回日期:2008-07-07 出版日期:2009-04-25 发布日期:2009-04-25
通信作者: 涂泳秋（1980-），女，博士，主要从事数字图像处理、模式识别研究． E-mail:tu．yongqiu@mail．scut．edu．cn
作者简介:涂泳秋（1980-），女，博士，主要从事数字图像处理、模式识别研究．
基金资助:
国家自然科学基金资助项目（60573019）

Mixed-Noise Removal Based on L＆A-PCNN

Tu Yong-qiu¹ Li Shao-fa¹ Wang Cheng¹ Wang Min-qin²

1. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China; 2. School of Computer Science, Zhaoqing University, Zhaoqing 526061, Guangdong, China

Received:2008-05-06 Revised:2008-07-07 Online:2009-04-25 Published:2009-04-25
Contact: 涂泳秋（1980-），女，博士，主要从事数字图像处理、模式识别研究． E-mail:tu．yongqiu@mail．scut．edu．cn
About author:涂泳秋（1980-），女，博士，主要从事数字图像处理、模式识别研究．
Supported by:
国家自然科学基金资助项目（60573019）

摘要/Abstract

摘要： 现有脉冲耦合神经网络模型普遍存在阂值函数复杂、用于图像平滑时图像信息易丢失以及易产生污斑等缺陷．为此，文中设计了一种阈值线性衰减的输出带权均值型PCNN模型，简称L＆A—PCNN．通过数学推理和实验获得了L＆A-PCNN的关键参数的最优选取范围，并将L＆A—PCNN与中值滤波器结合对图像去噪领域的难点——混合噪声进行修复．仿真实验结果证明，L＆A—PCNN算法的去噪性能比现有算法提高了5％～30％．

关键词: 脉冲耦合神经网络模型, 线性衰减阈值, 点火像素, 带权均值, 混合噪声, 中值滤波

Abstract:

As the existing pulse-coupled neural network （PCNN） suh in blur patch and information loss during image smoothing, a models are of complex threshold functions and may remodified PCNN model L＆A-PCNN with linear-attenuated threshold and weighted average gray level output is designed. The optimal value ranges of the key parameters of the new model are then determined via mathematical reasoning and experiments. Moreover, the mixed noise which is difficult to denoise is recovered by combining the L＆A-PCNN model with a median filter. Simulated results show that the denoising performance of the new algorithm improves by 5% -30%, as compared with the existing algorithms.

Key words: pulse-coupled neural network model, linear-attenuated threshold, firing pixel, weighted average, mixed noise, median filtering

涂泳秋黎绍发王成王敏琴 . 基于L＆A—PCNN模型的混合噪声滤除[J]. 华南理工大学学报（自然科学版）, 2009, 37(4): 31-36.

Tu Yong-qiu Li Shao-fa Wang Cheng Wang Min-qin. Mixed-Noise Removal Based on L＆A-PCNN[J]. Journal of South China University of Technology (Natural Science Edition), 2009, 37(4): 31-36.