基于非下采样Shearlet变换的磁瓦表面裂纹检测

Detection of Surface Crack Defects in Magnetic Tile Images Based on Nonsubsampled Shearlet Transform

针对磁瓦表面裂纹缺陷图像背景不均匀、对比度低和存在纹理干扰等特点,提出了一种基于非下采样Shearlet变换(Nonsubsampled Shearlet transform,NSST)的裂纹检测方法。首先对原始图像进行多尺度、多方向NSST分解,得到一个低频子带和多个高频子带,然后利用各向异性扩散和改进的γ增强方法对高频子带进行滤波和增强;同时利用二维高斯函数对低频子带进行卷积操作来构造高斯多尺度空间,估计出图像的主要背景,并通过背景差法得到均匀的低频目标图像。最后通过重构NSST系数得到去噪和增强后的均匀目标图像,利用自适应阈值分割和区域连通法提取裂纹缺陷。实验结果表明,所提方法检测准确率达92.5%,优于基于形态学滤波方法、基于Curvelet变换方法和基于Shearlet变换方法等现有磁瓦表面裂纹检测方法。

A novel algorithm based on nonsubsampled Shearlet transform （NSST） , Gaussian multi-scale space and anisotropic diffusion was proposed for detecting crack defects with uneven background, low contrast, noise corruption and textured interference in magnetic tile surface images. Firstly, NSST was employed to decompose the source magnetic tile image into one low-pass subband and a series of high- pass subbands. Then the anisotropic diffusion and the modified y enhancement method were applied to remove the noise and enhance the weak object information in the high-pass suhbands, respectively. Meanwhile, the background was estimated in the Gaussian multi-scale space constructed by convolving the low-pass subband with a varied two-dimensional Gaussian functions, and the even low-pass object could be obtained by using background subtraction. Finally, inverse NSST was utilized to reconstruct the enhanced object image which was free from noise and grinding texture interference, and crack defects could be segmented from the reconstructed image by applying the adaptive threshold method and regional connectivity function. Experimental results demonstrate that compared with four existing methods （OTSU method, method based on the adaptive morphological filtering, method based on Curvelet transform and texture feature measurement and method based on Shearlet transform） , the proposed method achieves better performance in terms of defect detection accuracy.