摘要
结合非下采样剪切波变换域三变量阈值滤波和多分辨引导滤波,本文提出一种去除高斯白噪声的图像去噪的有效方法。在非下采样剪切波变换域中,以三变量非高斯模型对方向带通子带系数间相关性进行建模,采用最大后验估计理论推导出三变量收缩阈值函数。此外,对低频子带系数采用多分辨引导滤波进行平滑处理,以达到更好的噪声抑制效果。实验结果显示,本文所提去噪方法可以有效抑制噪声同时保留更多的图像细节信息,与其他滤波算法相比,该去噪算法可得到更高的客观数据及更好的视觉效果。
We present an efficient algorithm for removing white Gaussian noise from corrupted images by incorporating a nonsubsampled Shearlet transform (NSST)-based trivariate shrinkage filter into a multiresolution guide filter. In the NSST domain, coefficients are modeled as a trivariate Gaussian distribution, accounting for the statistical dependencies among interscale and intrascale transform coefficients. A nonlinear trivariate shrinkage function is derived using a maximum a posteriori (MAP) estimator. To obtain better denoising results, low-frequency sub-bands are smoothed using a multiresolution guide filter. Experimental results show that our algorithm is very effective in eliminating image noise, and performs better than other denoising techniques.
出处
《红外技术》
CSCD
北大核心
2017年第11期1045-1053,共9页
Infrared Technology
基金
安徽科技学院校级项目(ZRC2016499)
安徽省自然基金资助项目(1508085MC55)
安徽省教育厅自然科学重点项目(KJ2016A174)
关键词
图像去噪
非下采样剪切波变换
三变量非高斯模型
引导滤波
image denoising, nonsubsampled Shearlet transform, trivariate non Gaussian model, guided filter