基于NSCT和全变差模型的医学图像去噪(英文) 被引量：3

Medical Image Denoising Using Non-subsampled Contourlet Transform and Total Variation Model

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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.

作者马秀丽周峰周小军

机构地区上海大学通信与信息工程学院上海大学智慧城市研究院

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）

分类号 TP391 [自动化与计算机技术—计算机应用技术]

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1CHANG S G, Yu B, VETTERLI M. An adaptive wavelet thresholding for image denoising and compression[J].IEEE Transactions on Image Processing, 2000: 15~2-1546.
2Do M N, VETTERLI M. The Contourlet transform: an efficient directional multiresolutional image representation[J].IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
3DA CAL, ZHOU J, Do M N. The nonsubsampled Contourlet transform: theory, design, and applications [J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101.
4ESLAMI R, RADHA H. Translational-invariant Contourlet transform and its application to image denoising [J]. IEEE Transactions on Image Processing, 2006, 15(11): 3362-3374.
5Po D D Y, Do M N. Directional multiscale modeling of images using the Contourlet transform [J]. IEEE Transactions on Image Processing, 2006, 15(6): 1610-1620.
6KANITHI Anil Kumar. Study of spatial and transform domain filters for efficient noise reduction [D]. National Institute of Technology, India, 2011.
7STEIDL Gabriele, WEICKERT Joachim. Relations between soft wavelet shrinkage and total variation de noising [C]/ /LNCS 2449, 2002: 198-205.
8Y AN Jie, Lu Wusheng. Image denoising by generalized total variation regularization and least squares fidelity [J]. Multidimensional Systems and Signal Processing, 2013.
9OSHER S, SOLE A, VESE L. Image decomposition and restoration usin~ total variation minimization and the H-1 Norm [J].SIAM Journal of Multiscale Modeling and Simulation, 2003, 1(3): 350-369.
10WANG Y, YANG J, YIN W, ZHANG Y. A new alternating minimization algorithm for total variation image reconstruction [J]. SIAM Journal on Imaging Sciences, 2008, 1(3): 248-272.