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《Journal of Data Acquisition and Processing》 2011-04
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WBCT and Variational Regularization Method for Medical Image Denoising

Wen Qiaonong,Wan Suiren(Medical Electronics Laboratory,Southeast University,Nanjing,210096,China)  
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.
【Fund】: 国家重点基础研究发展计划(“九七三”计划)(2010CB933903)资助项目
【CateGory Index】: TP391.41
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