基于数学形态学的Contourlet变换域图像降噪方法

提出了一种基于数学形态学的Contourlet变换域图像降噪方法.首先对输入的带噪图像进行多尺度、多方向的Contourlet稀疏变换,然后利用数学形态学算子在Contourlet域对高频系数进行处理,去除图像中具有较小支撑域的噪音,有效保留具有连续支撑域的图像边缘信息.最后通过Contourlet反变换得到预降噪图像.仿真结果表明,该方法较一般的Contourlet域收缩阈值降噪方法的降噪效果好,提高了PSNR值并降低了MSE值,获得更好的图像恢复质量.

A Contourlet Domain Image Denoising Method Based on Mathematical Morphology

A Contourlet domain image denoising method is proposed based on mathematical morphology. By using Contourlet Transform, the noised image is decomposed into a low frequency subband and a set of multisacle and multidirectional high frequency subbands. The high frequency coefficients of the original image are processed by mathematical morphological operator. The noise which have small or no at all support area are removed, and the small features which have large or consecutive support area are preserved. The denoising image will be gotten by performing the inverse Contourlet Transform to these estimated coefficients. Experimental results show that the denoising effect of this proposed method is better than that of other methods based on Contourlet transform.