在纺织品自动检测过程中，采集的图像容易受到噪声及织物表面材质的干扰，为解决这一问题，提出一种混合方法进行纺织品缺陷检测，将图像增强和缺陷检测方法进行混合处理，在非局部均值滤波算法（NLM）的相似度评价中引入主成分分析（PCA）进行去噪处理，采用的PCA-NLM混合模型有效增强了缺陷区域的灰度共生矩阵纹理特征，提高了缺陷纹理和无缺陷纹理之间的类可分离性。通过对7类缺陷的纺织品图像检测实验分析表明，相比单一的非混合方法，本文的混合模型有效提高了纺织品缺陷的检测正确率。

Fabric images are easily disturbed by noise and different surface materials. A hybrid approach is proposed to realize effective automatic fabric detection in this paper. Image enhancement and defect detection are combined to solve these problems. Principal component analysis (PCA)is introduced into the similarity evaluation in the nonlocal average filtering algorithm (NLM)to deal with the de-noising process. The GLCM (gray level co-occurrence matrix)texture features are improved by the PCA-NLM (principal component analysis-non local means)algorithm,which increase the class separability between defect areas and non-defect areas. The experiments show that,compared to the non-hybrid methods, the proposed hybrid model can obtain a higher accuracy with seven class fabric defects.