摘要
提出了一种基于非下采样Contourlet变换(NSCT)域图像去噪算法.首先根据尺度间与尺度内的NSCT系数之间的相关性,用非高斯分布模型对NSCT系数与其邻域系数及父系数进行建模,给出分类准则,把系数分为重要系数和非重要系数,再采用广义高斯分布来模拟重要系数的概率分布,根据贝叶斯理论得到自适应阈值,并求出最佳参量范围.为了克服软、硬阈值函数的缺点,提出一种自适应的新阈值函数,利用新阈值函数估计出不含噪音的变换系数,并通过非下采样Contourlet逆变换得到去噪后的图像.仿真实验表明,本文方法在峰值信噪比、结构相似性与视觉效果上均优于目前许多优秀的去噪算法.
A novel image denoising algorithm based on nonsubsampled Contourlet transform domain is proposed.First,according to the correlation of nonsubsampled Contourlet transform coefficients in interscale and intrascale,non-Gaussian distribution model is used to model its correlations.We propose a classification standard where the coefficients are divided into important and unimportance coefficients,and generalized Gaussian distribution is used to describe the probability distribution for the important coefficients.Adaptive threshold is derived under the Bayesian theory and the best range of the parameter is found out.In order to overcome the shortcoming of the soft and hard thresholding function,then a new adjustable thresholding function is presented.Lastly,the new thresholding function is used to estimate coefficients without noise,and inverse nonsubsampled Contourlet transformation is performed to get denoised image.Experimental results show that our algorithm outperforms the other current outstanding algorithms in peak signal-to-noise ratio,structural similarity and visual quality.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2012年第6期751-756,共6页
Acta Photonica Sinica
基金
安徽省教育厅重点科研项目(No.KJ2010A282)
安徽省自然科学基金(No.11040606M06)资助
关键词
非下采样CONTOURLET变换
非高斯分布
广义高斯分布
峰值信噪比
结构相似性
Nonsubsampled Contourlet transform(NSCT)
Non-Gaussian distribution
Generalized Gaussian distribution
Peak signal-to-noise ratio(PSNR)
Structural similarity(SSIM)
