计算机工程与应用 ›› 2013, Vol. 49 ›› Issue (15): 150-156.

• 图形图像处理 • 上一篇    下一篇

图像脉冲噪声检测

罗海驰,李岳阳,孙  俊   

  1. 江南大学 轻工过程先进控制教育部重点实验室,江苏 无锡 214122
  • 出版日期:2013-08-01 发布日期:2013-07-31

Impulse noise detection method for images

LUO Haichi, LI Yueyang, SUN Jun   

  1. Key Lab of Advanced Process Control for Light Industry(Ministry of Education), Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2013-08-01 Published:2013-07-31

摘要: 提出了一个包含两个自适应神经模糊推理系统和一个后处理块的网络,该网络可用于灰度图像脉冲噪声检测。网络中每个自适应神经模糊推理系统都是一个四输入单输出一阶Sugeno模糊推理系统。所提出的脉冲噪声检测方法分两步进行:对该网络进行优化训练,确定其参数;用优化后的网络对被椒盐脉冲噪声污染的图像进行噪声检测。实验结果表明,与其他传统检测方法相比,所提出的方法,更能有效检测出图像中椒盐脉冲噪声。

关键词: 噪声检测, 神经模糊推理系统, 脉冲噪声

Abstract: A neuro-fuzzy network approach to impulse noise detection for gray scale images is presented. The network is constructed by combining two neuro-fuzzy blocks with a postprocessor. Each neuro-fuzzy block is a first order Sugeno type fuzzy inference system with 4-inputs and 1-output. The proposed impulse noise detector consists of two modes of operation, namely, training and testing. As demonstrated by the experimental results, the proposed detector significantly outperforms other conventional detectors.

Key words: noise detection, neuro-fuzzy inference system, impulse noise