中文摘要：

提出一种基于非下采样Shearlet变换（NSST）的各向异性双变量收缩函数的图像去噪算法。根据NSST不同尺度间系数的方差各向异性特性,在双变量收缩函数的基础上引入各向异性拉普拉斯概率分布,利用牛顿迭代算法得到各向异性的双变量收缩函数,对NSST系数进行处理,充分利用NSST能捕捉更多纹理及结构等细节信息的优点。实验结果表明,该算法在峰值信噪比、结构相似性以及主观视觉效果上均得到较大提高。

英文摘要：

A method for image denoising based on the combination of the anisotropic BiShrink and non-subsampled Shearlet transform（NSST）was proposed.Considering the anisotropic property of the variances of NSST coefficients in different scales,an anisotropic Laplacian distribution function was introduced on the foundation of BiShrink.A threshold function was derived from Newton’s method to obtain the denoised coefficients,takeing full advantage of NSST could capture texture and structure information more effectively.Extensive experimental results demonstrate that the proposed method can obtain better performance in terms of peak signal-to-noise ratio,structural similarity and subjective evaluations than some current outstanding denoising methods.