摘要
在分析了斑点噪声和PCNN的特点的基础上,将PCNN引入到小波域中,并结合小波软阈值去噪思想,提出了基于PCNN的超声医学图像软阈值去噪方法(ST-PCNN),该方法的优点是实现了在小波域中利用PCNN来识别高频信号的小波系数,并采用相应的方法处理小波系数,改善了PCNN难以确定斑点噪声的位置和采用固定阈值造成高频信号损失的缺点,更好的保留了低于固定阈值的高频信号的小波系数;在此基础上,将模糊算法引入到PCNN模型中,进一步提出了基于模糊PCNN的小波域超声医学图像去噪方法(F-PCNN-WD),该方法利用模糊算法来去除PCNN点火过程中大于点火阈值的斑点噪声的小波系数,以更好的去除斑点噪声。实验结果表明,ST-PCNN和F-PCNN-WD方法不仅能够有效地去除噪声,而且能够很好的保留图像的边缘和细节信息。
Based on the analysis of the speckle noise and PCNN′s properties,PCNN is introduced in to wavelet domain,by combining the thought of the solft-threshold de-noising,Soft-threshold de-noising method of medical ultrasonic image based on PCNN（ST-PCNN） is proposed.The advantage of ST-PC-NN is that PCNN recognizes the coefficients of high frequency in wavelet domain are realized,and then the wavelet coefficients are processed by corresponding methods.ST-PCNN improves the disadvantage that PCNN can not accurately determine the position of speckle noise and the fixed threshold makes some high-frequency signals loss,and better reserves the wavelet coefficients of high frequency signal which are lower than the fixed threshold.On this basis,fuzzy algorithm is applied in the model of PC-NN,method of medical ultrasonic image de-noising based on Fuzzy PCNN in the Wavelet Domain（F-PC-NN-WD） is proposed.The proposed method make use of fuzzy algorithm to remove the wavelet coeffi-cients of speckle noise which are greater than the ignition threshold value of PCNN,so the speckle noise can be better removed.The experimental results show that ST-PCNN and F-PCNN-WD can not only remove the noise but also reserve the detail information and the image edge.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2010年第3期476-480,共5页
Journal of Optoelectronics·Laser
基金
江苏省自然科学基金资助项目(BK2009410)
全国优秀博士学位论文作者专项资金资助项目(200753)
江苏省高等学校自然科学基金资助项目(08KJB510010
07KJB510068)
江苏省"六大人才高峰"培养对象资金助项目南京信息工程校级科研机构创新团队启动资金资助项目(JG0803
TD0810)
