基于灰色系统理论的各向异性扩散图像去噪方法

Anisotropic Diffusion Image Denoising Based on Grey System Theory

为克服各向异性扩散图像去噪算法对弱边界和弱细节保留不足,提出一种融合灰色关联度的各向异性扩散图像去噪方法,通过计算图像像素点局部邻域的灰色关联度,利用灰色关联度确定该点邻域是边缘区域,还是平滑区域,采用不同的扩散系数对图像去噪。实验结果表明,该算法能有效地去除图像噪声,同时能够保留图像细节信息,为研究灰色关联理论用于图像处理提供了有效途径。

In order to overcome the shortage of anisotropic diffusion image denoising algorithm for weak boundary and details, the author puts forward a kind of anisotropic diffusion image denoising method of fusion of grey correlation degree. Through calculating the grey correlation degree of local neighborhood of image pixels, by which determining its neighborhood is edge of the area or the smooth area, the image noise is removed by using different diffusion coefficient. The experimental results show that the algorithm can effectively remove the image noise, preserve image details, and provide an effective way to study the gray correlation theory.