中文摘要：

针对小波空间Donoho阈值在图像去噪中的缺陷，提出一种基于平稳小波变换的自适应阈值MR图像去噪方法，即由Lakhwinder Kaur小波阈值选取法，根据不同的子带特性，定义了一个新的尺度参数方程，以确定适合各个尺度级的自适应最优阈值，对平稳小波变换后的各层细节信号分剐进行阅值化处理.该方法能很好的抑制小波空间Donoho阈值去噪法出现的伪Gibbs现象，弥补了正交小波变换存在的不足，在滤出噪声的同时，较好地保留了MR图像的细节信息．实验结果表明该算法在性能指标和视觉质量上的优越性。

英文摘要：

To overcome the defect of image denoising based on Donoho＇ s threshold of wavelet domain, a self-adaptive method for image denoising based on stationary wavelet transformation was proposed. According to different subband characteristics, a new scale parameter equation was defined to determine the optimal thresholds adapting to each step scale, and the thresholds of the detail signals of each level generated by stationary wavelet transformation were obtained. The method not only well depresses the Gibbs impact well, but also supplies a gap of ort.hogonal wavelet transformation so as to retain the MR image details when wiping off noise. Experimental results indicate that the proposed method is more effective than discrete orthogonal wavelet transformation with respect to performance and visual effect.