平移不变的小波域近邻系数阈值MR图像去噪

传统的小波域阈值去噪方法是根据每个小波系数各自的幅度大小进行相应的阈值修正，没有考虑到尺度间以及尺度内近邻的小波系数与当前小波系数的相关性，而使信号得不到更准确的估计。根据信号和噪声在尺度间的不同传播特性和尺度内近邻小波系数的相关性，设计出一种平移不变（TI）的近邻系数阈值策略，并依据磁共振成像（MRI）噪声图像的特点，结合复数域统一体去噪方法，提出了一种新颖的基于平移不变的小波域近邻系数阈值MR图像去噪算法。实验表明该算法能更准确地估计信号，且与几种磁共振（MR）图像去噪算法相比具有更好的去噪效果。

MR Images Denoising Based on Neighboring-coefficients Thresholding in Wavelet-domain

In standard wavelet methods, the empirical wavelet coefficients are thresholded term by term, on the basis of their individual magnitudes. Information on other coefficients between the different scales and in the same scale has no influence on the treatment of particular coefficients, resulting in the lower accuracy of signal estimation. A translation-invariant (TI)neighboring-coefficients thresholding is designed by incorporating the different evolution of signal and noise along the scales of wavelet domain and information in the same scale. Considering the particularity of noise in magnetic resonance (MR)images, a novelty MR complex denoising algorithm based on TI neighboring-coefficients thesholding is developed by employing the complex entity method in MR complex images. The results of the simulated experiments show that the proposed algorithm has the higher accuracy of signal estimation, and outperforms previous MRI denoising methods about denoising capability.