摘要
分析了非下采样Contourlet变换(nonsubsampled Contourlet transform,NSCT)和全变差模型的特点,提出将NSCT和全变差混合模型应用于医学图像去噪.首先,通过NSCT变换将含噪图像分解,运用Visu萎缩阈值将NSCT系数进行处理,得到初次去噪图像.然后,采用全变差模型对初次去噪图像进一步处理得到最终去噪图像.实验结果表明:该方法可以很好地保留图像细节,无论在客观上的峰值信噪比还是主观上的视觉效果都优于其他去噪方法.
The characteristics of non-subsampled Contourlet transform （NSCT） and total variation （TV） modeling are analyzed. A mixed model of NSCT and TV is applied to medical image denoising in this paper. NSCT filter-based decomposition of noisy medical images is performed. An initial denoised image is produced using a Visu shrink threshold algorithm. The final denoised image is obtained by processing the initial denoised image with the TV model. Experimental results show that the image details are well preserved by using the proposed method. Both peak signal-to-noise ratio （PSNR） and visual quality are superior to some other denoising algorithms.
出处
《应用科学学报》
CAS
CSCD
北大核心
2014年第5期481-485,共5页
Journal of Applied Sciences
基金
Project supported by the National Science Foundation of China(No.61103076)
the Shanghai Municipal Natural Science Foundation(No.12ZR1410800)
the Innovation Program of Shanghai Municipal Education Commission(No.13YZ016)
the "863" National High Technology Research and Development Program of China(No.2013AA01A603)
关键词
非下采样CONTOURLET变换
全变差
医学图像去噪
峰值信噪比
non-subsampled Contourlet transform, total variation, medical image denoising, peak signal-to-noise ratio （PSNR）
