非下采样Contourlet变换域统计模型红外图像去噪

摘要

Infrared image with white Gaussian noise is processed by nonsubsampled Contourlet transform. The statistical characteristic of its coefficients is analyzed and generalized Gaussian distribution is used to describe the probability distribution for coefficients. According to characteristics of different energies in each direction of the nonsubsampled Contourlet transform bandpass subbands, a modified Bayesian threshold formula is proposed. In order to overcome the shortcoming of the soft and hard thresholding function, then a new adjustable and adaptive 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 infrared image. Experimental results show that our denoising algorithm outperforms the usual wavelet threshold denoising method in peak signal-to-noise ratio and visual quality.%对红外图像进行非下采样Contourlet变换,分析其系数的统计特征,采用广义高斯分布来模拟系数的概率分布.根据非下采样Contourlet变换的带通子带各方向能量不同的特点,提出修正的贝叶斯阈值公式,为了克服软、硬阈值函数的缺点,又提出一种具有可调节自适应性的新阈值函数,最后利用新阈值函数估计出不合噪声的变换系数,并通过非下采样Contourlet逆变换得到去噪后的红外图像.仿真实验表明,文中方法在峰值信噪比及视觉效果上均优于经典的小波阈值去噪算法.