基于子带收敛因子阈值法的轮廓波消噪方法

Contourlet Transform Denoising Method Based on Subbands Convergence Factor Threshold

提出了适用于轮廓波变换消噪中确定子带阈值收敛因子的样本噪声响应法。该方法根据标准高斯白噪声作用在每个子带上的统计特性,得到每个子带的收敛因子;使用该收敛因子对标准的3σ(或4σ)准则进行修正来确定不同尺度不同方向子带的硬阈值;并在轮廓波域进行子带硬阈值处理之后,使用自适应维纳滤波进行后处理。实验结果表明,本文提出的消噪方法,对含有高斯白噪声的图像进行消噪,无论在峰值信噪比方面还是在视觉效果方面均可以取得比较满意的消噪效果;在一定的范围内,采用较小的样本图像计算子带收敛因子,在加快消噪速度和减小内存需求量的同时,仍然可以保持满意的消噪结果。

Sample noise response method used to determine subband threshold factors of contourlet transform denoising is proposed. By the method, we can obtain the convergence factor of each subband according to every subband statistical character driven by a standard Gaussian white noise. The hard threshold of every directional subband of each scale is determined by modifying the 3a （or 40） rule in terms of corresponding effect factor. The post-processing can be carried out by adaptive Wiener filtering followed subband hard threshold in contourlet domain. Experimental results show that, using the denoising method proposed, for images corrupted by Gaussian white noise, the denoising results including Peak Signal-noise Ratio （PSNR） and quality of visual effect are satisfying. To calculate the convergence factors using smaller sample image can accelerate the denoising speed and reduce the requirement of the memory for the program, and the denoising results can be kept satisfied.