基于Contourlet变换的图像DCT去噪新方法

为了改进滤波效果,以提高图像去噪质量,提出了一种通过离散余弦变换对Contourlet域中噪声能量进行估计来实现去噪的新方法.该算法不依赖于对噪声方差进行估计,而是直接利用离散余弦变换来对高频各子带进行局部特征提取,以便估计噪声能量的估计阈值.实验结果表明,与传统的小波软、硬阈值去噪方法和基于小波变换的图像离散余弦变换去噪方法比较,该方法有效地克服了采用硬阈值法引起的伪吉布斯现象和软阈值法因导致过度光滑而使信号失真等缺点.实验表明,该算法不仅可提高处理图像的信噪比,而且图像的视觉效果也明显改善,因此更具有实用价值.

A New Method Using DCT Based on Contourlet Transform for Image De-noising

To improve image de-nosing effect,in this paper,a new method based on discrete cosine transform (DCT) in Contourlet domain is proposed,which utilizes noise energy rather than relying on accurate estimation of noise variance to perform image de-noising.This new method not only overcomes the psuedo-Gibbs phenomenon of the hard-thresholding method but also can avoid signal distortion which is caused by the soft-thresholding.Experiments show that the performance of the proposed method is obviously superior to other methods both in vision and in PSNR and is of more practical use.