针对随机纹理表面缺陷检测问题，提出了一种基于Gabor小波的检测方法。该方法首先利用实值2维 Gabor小波对图像进行多通道滤波；然后通过对滤波图像进行非线性处理和平滑滤波产生通道能量图像（特征图像）；接着在学习阶段估计学习样本（不含缺陷）特征的统计参数，并用于指导检测阶段特征图像的阈值化；最后在不同尺度和方向，对阈值化后的特征图像进行融合，并二值化，以达到减小虚警率的目的。实验结果表明，该方法检测效果好，且要求学习样本少，适用于不同缺陷类型和各种检测问题。

A defect detection approach based on Gabor wavelets is proposed. Acquired image is multi-channel filtered with a bank of 2-D real Gabor filters. Next, feature images are obtained by subjecting each filtered image to a nonlinear transformation and computing a measure of energy through smoothing filter. In the learning phase, statistical parameters are computed using learning sample which is free of defect and are used to supervise the thresholding of inspected image. Finally, threshold-fietered feature images are fused inter-scale and inter-orientation and binarized in order to decrease false alarms. Experiments demonstrated that this approach has good detection ability performance and needs less learning samples, which makes it suitable for many types of defect and textured material.