## 4 Experiments and Discussion
### Comparative Experiments of Different Methods  
In order to evaluate the effectiveness of our method, we carried out comparative experiments between the proposed method and three other unsupervised methods: PHOT [37], AnoGAN [31] and MSCDAE [30]. All methods were trained and tested with the same dataset. The inspection results over eight different kinds of texture sample are illustrated in Figures 7-9. The PHOT method is based on an observation in the Fourier representation of signals where spectral phase can retain many important features of original signals; this does not work for spectral magnitude. The PHOT method always introduces a high alarm rate because of the global reconstruction scheme (Figure 7a,c, Figures 8a and 9a). The second method, AnoGAN, applies generative adversarial network to model the manifold of the defect-free samples and an inverse mapping from image space to latent space is conducted to generate the corresponding reconstructed image for a given query sample. Then a pixel-level comparison is used to realize segmentation of anomaly area. It does not consider the spatial dependencies among pixels and achieves a considerably high false positive rate, as shown in Figures 7c, 8b and 9a. The third method, MSCDAE, is another deep learning-based method, but its basic component is convolutional autoencoder. Its reconstruction ability is realized through CDAE and a Gaussian pyramid is introduced to improve the robustness of the final inspection result. The MSCDAE method exhibits good performance in images where there is a great difference in gray level between defect and background, as shown in Figure 7a,b,d, Figures 8a and 9c, but performs poorly in Figure 8b,c due to the imperfect reconstruction ability of CAE. In contrast to the above three, our method employs adversarial learning to enhance image reconstruction ability, and introduces a regional analysis module to reduce the false positive rate. It showed superior performance in almost all images, as shown in the last row from Figures 7-9. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Recall** & **Precision** & **F1-Measure** \\ \hline Recon + Morph & 0.9423 & 0.4995 & 0.6578 \\ Recon + LDA & 0.9633 & 0.5704 & 0.7106 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of different detection methods. Figure 6: The influence of the local difference analysis module. (**a**) Original image; (**b**) Residual image; (**c**) Segmentation result only with morphological operation; (**d**) Segmentation with LDA. Figure 8: Segmentation results of defective samples with three kinds of textural background from Patterned Fabric dataset under different methods. Each row from left to right are original defect image, ground truth image, results derived from PHOT, AnoGAN, MSCDAE, and our methods. (**a**) box; (**b**) dot; (**c**) star. Figure 7: Segmentation results of defective samples with four kinds of textural background from TILDA Textile Texture dataset under different methods. Each row from left to right are original defect image, ground truth image, results derived from PHOT, AnoGAN, MSCDAE, and our methods. (**a**) cl1; (**b**) \(\mathcal{O}\); (**c**) \(\mathcal{O}\); (**d**) \(\mathcal{O}\). The quantitative inspection results of the four methods are exhibited in Tables 4-6, where the bold data indicates better results. It can be seen that the previous three methods always fail in some cases. Specifically, the PHOT method has comparably low F1-measure values in Figures 7a-c and 8a; its global reconstruction strategy resulted in many falsely detected regions. The AnoGAN method has very low precision values in almost all cases, because it merely adopts a global reconstruction scheme and ignores the space connection. The MSCDAE method could hardly segment defects in Figure 8b,c because of its poor reconstruction ability over complex texture images. The proposed method achieved superior segmentation performance in almost every test.