摘要
针对脉冲噪声和高斯噪声构成混合噪声的特点,提出了一种基于简化型脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)滤波算法,利用了简化模型的几个技术特性,适当的选取参数,并在算法中结合了形态学方法、中值滤波和维纳滤波,该算法相对于均值滤波和中值滤波算法来说具有更好的抑制混合噪声干扰的能力,能较好地保持图像的边缘和细节信息。通过大量实验证实,应用简化型PCNN滤波算法对滤除灰度图像所受混合噪声的效果较好。在与其他算法的比较中,该算法优于传统的滤波算法,不但能有效地滤除混合噪声,并且计算量适中,具有较好的实时性,同时随着图像受混合噪声污染程度的增大,优势更加明显。
To the speciality of mixed noise constituted by pulse noise and Gauss noise, we present a comprehensive algorithm in this text, which is based on the simplified PCNN model, utilizing several technique specialities of the model, selecting parameters properly, and combining with mathematical morphology method, median filtering and wiener filtering. This method performs better than average filters and median filters on hybrid noise reduction while retaining edges and detail information of the image. Experiments show that the effect of eliminating grey image mixed noise which applying the simplified PCNN eliminating algorithm proposed in this paper is good. This algorithm can show a big advantage when in the comparison with other algorithms. This algorithm not only can effectively filter hy- brid noise but also can excel in real-time tasks because of its reduced computation complexity. With the increase of the image populated by blends noise, the advantage is obvious.
出处
《控制工程》
CSCD
北大核心
2013年第5期829-832,共4页
Control Engineering of China
基金
辽宁省教育厅资助项目(2010095)
