带钢表面缺陷在线检测系统的图像滤波算法  

An Image Filtering Algorithm for Online Detection System of Steel Strip Surface Defects

作　　者：刘伟嵬[1] 颜云辉[1] 李瞻宇 李骏[1] 

机构地区：[1]东北大学机械工程与自动化学院,辽宁沈阳110004 [2]中国联通有限公司东莞分公司,广东东莞523009

出　　处：《东北大学学报（自然科学版）》2009年第3期430-433,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(50574019);国家高技术研究发展计划项目(2008AA04Z135)

摘　　要：由于纹理背景的存在导致图像缺陷检测结果不准确是目前冷轧带钢表面缺陷在线检测过程中存在的主要问题之一.针对该问题提出了一种基于小波的各向异性扩散图像滤波方法,该方法通过小波分解得到图像的低频和高频分量,并针对高频分量采用小波扩散系数对小波系数进行正则化处理,最后进行小波逆变换,重构滤波后图像.实验结果表明该方法不仅能够有效地滤除不必要的纹理背景信息,而且能够较好地保留图像的细节信息,具有更好的综合性能,为带钢表面缺陷在线检测系统中的后续处理,如图像的边缘检测和自动分割等奠定了基础.Due to the complexity of surface texture, the images obtained from the existing online detection system cannot show the strip surface defects exactly, which becomes one of the important problems to be solved for the detection of surface defects of cold-rolled strip. A novel wavelet-based image filtering algorithm by virtue of anisotropic diffusion is therefore proposed. It decomposes the original image into the low and high-frequency components by wavelet transform, then the high-frequency components are regularized by wavelet diffusion coefficients and, finally, the filtered image is reconstructed by inverse wavelet transform. To achieve a satisfactory filtering result, the wavelet-based anisotropic diffusion is often performed iteratively. Experimental results indicated that this new algorithm could not only filter off the unnecessary texture background but also preserve the valuable information in detail effectively. With more favorable combinability in filtering, this algorithm will lay a solid foundation for the subsequent image processing, e.g. image edge detection, image auto-segment, etc.

关 键 词：图像滤波 图像处理 缺陷检测 各向异性扩散 小波变换 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]