Shearlet变换与核各向异性扩散的图像噪声抑制

摘要

To suppress noise of image more efficiently and further improve image visual effects, a noise suppression method of image based on shearlet transform and kernel anisotropic diffusion is proposed. Firstly, a noisy image is decomposed by nonsubsampled shearlet transform(NSST). Then the obtained low⁃frequency component and high⁃frequency components are processed by improved total variation ( ITV) diffusion and kernel anisotropic diffusion (KAD), respectively. Finally, the noise suppressed image is obtained by synthesizing diffused low⁃frequency component and high⁃frequency components through inverse nonsubsampled shearlet transform(INSST). Experimental results are given, in terms of subjective visual effect and two quantitative evaluation indicators such as peak signal to noise ratio (PSNR), structural similarity (SSIM), a comparison is made with three recent proposed noise suppression methods based on wavelet threshold shrinkage and TV, based on nonlinear diffusion in complex contourlet domain, and using TV with adaptive shearlet domain restraint. A large number of experimental results show that the proposed method has stronger noise suppression ability and preserves edge and detail information more completely.%为了更有效地抑制图像噪声，改善图像视觉效果，提出了一种基于非下采样Shearlet变换（ nonsubsampled shear⁃let transform，NSST）与核各向异性扩散的图像噪声抑制方法。首先对含噪图像进行非下采样Shearlet变换；然后对所得到的低频和高频分量分别进行改进的全变差（ improved total variation， ITV）扩散与核各向异性扩散（ kernel anisotropic diffu⁃sion， KAD）；最后对扩散后的低频和高频分量进行非下采样Shearlet逆变换得到噪声抑制后的图像。给出了实验结果，并且依据主观视觉效果和峰值信噪比、结构相似度两种定量评价指标，与近年来提出的基于小波阈值收缩结合全变差、基于复Contourlet域非线性扩散、自适应Shearlet域约束的全变差等3种噪声抑制方法进行了比较。实验结果表明，该方法的噪声抑制能力更强，且更为完整地保留了图像的边缘和细节信息。