基于Contourlet域非线性扩散的织物疵点图像去噪

Fabric defect image de-noising based on contourlet transform and nonlinear diffusion

提出了将Contourlet变换与非线性扩散相结合的织物疵点图像去噪方法。首先对图像进行Contourlet分解,然后高频部分和低频部分分别采用自适应对比度扩散和全变差扩散,最后重构图像。给出了实验结果,并与现有的小波阈值收缩和全变差扩散结合的方法、基于改进的Contourlet变换的自适应对比度扩散方法、小波变换与非线性扩散相结合的方法的图像去噪效果进行了比较。结果表明,经所提方法去噪后的图像获得的PSNR平均增益可达7.45 dB,去噪效果较为优越:不但抑制噪声的能力更强,而且能够更好地保留织物图像原有的边缘和纹理特征。

A fabric defect image de-noising method combining contourlet transform with nonlinear diffusion is proposed.Firstly,an image is decomposed by contourlet transform.Then adaptive-contrast-factor diffusion and total variation diffusion are applied to high-frequency component and low-frequency component,respectively.Finally the image is synthesized.The experimental results are given.A comparison of the image de-noising results is made with those of the image de-noising methods based on the combination of wavelet shrinkage with total variation diffusion,the combination of improved contourlet transform with adaptive-contrast-factor diffusion and the combination of wavelet transform with nonlinear diffusion.It is shown that the average PSNR gain of de-noised image can achieve 7.45 dB.The proposed image de-noising method can obtain superior results.It can both remove noise and preserve the original edges and textural features efficiently.