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.