光学精密工程2012，Vol.20Issue(9)：2060-2067,8.DOI:10.3788/OPE.20122009.2060
基于脉冲耦合神经网络模型的小波自适应斑点噪声滤除算法
wavelet adaptive denoising method based on PCNN
摘要
Abstract
The Wiener filtering principle and characteristics of a Pulse Couple Neural Network(PCNN) model were analyzed and a wavelet adaptive denoising method based on the PCNN (W-PCNN-WD) was proposed according to a statistical model of speckle noise combined with a wavelet transform to improve the quality of ultrasound image. Firstly, the ultrasound image was performed a log conversion to transform the speckle noise to an additive noise. Then, the Wiener filtering was used to process the medical image to get the variance of the additive noise as the threshold of wavelet. Furthermore, the image was preprocessed by the wavelet transform and wavelet coefficients were recomposed appropriately by using the PCNN. Finally, the image was processed again by the wavelet inverter and the exponential transforms to get a denoising image. The result shows that the proposed filtering method is better than the other filtering methods, and the Peak Signal to Noise RatioC PSNR) from the proposed method is higher 9 dB than that from the Wiener filtering when the noise variance is 0. 01. The method can keep the edge details of the information on the basis of removing speckle noise, which improvesthe visual quality of images greatly.关键词
斑点噪声/维纳滤波/脉冲耦合神经网络/小波变换Key words
speckle noise/ Wiener filtering/ Pulse Coupled Neural Network (PCNN)/ wavelet transform分类
信息技术与安全科学引用本文复制引用
李云红,伊欣..基于脉冲耦合神经网络模型的小波自适应斑点噪声滤除算法[J].光学精密工程,2012,20(9):2060-2067,8.基金项目
陕西省教育厅自然科学专项(No.12JK0512) （No.12JK0512）
西安工程大学博士科研启动基金资助项目 （）
