MRI is prone to noise pollution in imaging process.MRI with noise seriously affects the doctor's diagnosis of disease.In order to remove noise in MRI,this abstract considers a patch-based method that integrates Gaussian mixture models(GMMs) learning its parameters from external MRI patches with the clustering of desired patches guided by learned GMMs.The last step is to estimate the clear image by low-rank approximation process.Experimental results show the effectiveness of our method.Compared with the classical MRI denoising algorithm—NLM(Non Local Mean) and ADF(Anisotropic Diffusion Filter), our method achieves better results both visually and numerically.
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