WBCT与变分正则化方法的医学图像去噪  被引量:4

WBCT and Variational Regularization Method for Medical Image Denoising

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作  者:文乔农[1] 万遂人[1] 

机构地区:[1]东南大学医学电子学实验室,南京210096

出  处:《数据采集与处理》2011年第4期390-394,共5页Journal of Data Acquisition and Processing

基  金:国家重点基础研究发展计划("九七三"计划)(2010CB933903)资助项目

摘  要:在分析几种变分正则化去噪模型的基础上,改进了变分正则化去噪模型,它是各向异性扩散的,去噪效果好,但计算量较大。由于WBCT的阈值法去噪速度快,本文提出了混合去噪方法,充分利用两种方法的优点,先对噪声图像做WBCT,高频子带用WBCT的阈值法去噪,对低频子带用改进的变分正则法去噪,然后用WBCT逆变换重建图像。实验结果表明:这种混合去噪方法比单独用WBCT阈值去噪效果好,计算时间比单独使用正则化方法少,因此混合去噪方法的综合性能最好。Some variational regularization denoising models are analyzed, and a modified variational regularization denoising model is proposed, which is anisotropic diffusion and denoising effect with the disadvantages of the large amount of calculation. Since the wavelet-based contourlet transform (WBCT) threshold denoising is fast, a hybrid denoising method is presented, making full use of both advantages. Firstly, WBCT is maken for noise image. Then the WBCT threshold method denoising is used in the high-frequency sub-band and an improved variational regularization method denoising is applied to low-frequency sub-band. Finally, a reconstructed image can be obtained through inverse WBCT. Experimental results show that the denoising effect of the hybrid method is better than WBCT threshold and the computing time is less than the improved variational regularization method. Therefore, the hybrid denoising method proposed in this paper has the best comprehensive properties among the investigated denoising models.

关 键 词:WBCT 变分正则化模型 各向异性扩散 图像去噪 

分 类 号:TP391[自动化与计算机技术—计算机应用技术]

 

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