基于提升小波MRI图像自适应阈值去噪算法

分析了小波的消失矩特性对图像重构误差的影响，提出了利用提升算法提高双正交小波消失矩的改进算法。通过提升算法对传统小波提高消失矩，改善了小波的性能，使小波具有更好的振荡性，能够更好地捕捉图像的细节，从而提高了重构信号的精确度。根据磁共振图像的特点及其噪声的分布特性，提出了一种对小波系数进行分块处理的阈值去噪方法。通过对分解后每个层次上的各高频系数矩阵分为多个子矩阵分别进行不同阈值的选取，实现在不同的对比度区域选取不同的阈值的目的，从而使阈值的选取更具有自适应性。

Adaptive MRI denoising based on lifting wavelet

This paper analyzes how the property of the wavelet effect the construction error of the image and then propose a improved method to increase the vanishing moment of a wavelet by lifting algorithm.By increasing the vanishing moment,the performance of traditional wavelet is improved to have the ability of capturing more details with a better vibration,and thus consequently enhances the reconstruction precision.according to the MR image characteristic and the noise distribution property,a more self-adaptive threshold selection method is proposed to thresholding wavelet coefficients.On each decomposition level,the coefficient matrix of the high frequency is deblocked to several sub-matrixes.The decomposition level,the contrast and the absolute median of a selected sub-matrix are combined to determine the threshold used to process the corresponding coefficients of the sub-matrix.So the thresholds determined by this method have a better self-adaptive performance.A large number of experiments on MR image are performed.The simulation results indicate that the denoising algorithm on MR image proposed in this article obtains a better performance,especially for MR image with lower signal-noise ratio.