基于非下采样Contourlet变换的医学CT图像去噪

为克服Contourlet变换的非平移不变性及频谱混叠等缺陷，提出了一种基于非下采样Contourlet变换的医学CT图像去噪方法。对含噪的CT图像进行非下采样Contourlet变换，得到不同尺度及各个方向上的变换系数，利用Context模型将每个尺度每个方向子带分级，不同分级采用相应的阈值去噪。实验表明，该方法适宜于处理含有更多高斯噪声的医学CT图像，与其他方法相比提高了PSNR值，更好地保留了图像细节，改善了医学CT图像的质量。

Medical CT image denoising method based on nonsubsampled Contourlet transform

To overcome the Contourlet transform non translation invariance and spectrum aliasing defects,this paper presents a method based on nonsubsampled Contourlet transform for medical CT image denoising method.The noisy CT images are conducted by nonsubsampled Contourlet transform.Transform coefficients are obtained from different scales and different directions.Using Context model,subband of each scale and each direction is graded.Different classification uses the corresponding threshold denoising.Experiments show that this method is suitable to processing the medical CT image which contains more Gaussian noise.Compared with other methods,the PSNR value is improved,the image details are better retained,and CT image quality is improved.