# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 5 Conclusions  
In this study, an industrial image anomaly detection method based on noise-to-norm reconstruction is proposed. Our proposed method effectively solves the problem of invariant reconstruction of anomalous regions. We enhanced the M-net by incorporating a residual attention module and feature fusion, obtaining a reconstruction network. Our proposed method has significant advantages for handling data with multiple instances and varying object positions. Experimental results demonstrate that our method achieves SOTA performance in anomaly detection and localization on the MPDD dataset, and it also exhibits competitive performance on the VisA dataset.  
Our proposed method has limitations in detecting object deformation anomalies. Additionally, when there is noise in the background of certain images in the dataset, it is easy for us to misidentify it as an anomaly. In future work, we will explore methods that  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Coefficient** & \(\lambda=0\) & \(\lambda=0.2\) & \(\lambda=0.3\) & \(\lambda=0.4\) \\ \hline Bracket Black & 47.61/77.21 & 83.71/98.16 & **93.42**/98.97 & 90.56/**99.02** \\ \hline Bracket Brown & 82.50/83.93 & 90.57/91.84 & **93.14**/**93.10** & 84.09/92.86 \\ \hline Bracket White & 74.89/89.82 & **92.78**/**98.35** & 89.33/97.82 & 83.22/96.74 \\ \hline Connector & 96.67/93.70 & **100.00**/98.78 & **100.00**/**98.95** & **100.00**/98.80 \\ \hline Metal Plate & 97.83/97.88 & 98.92/**98.96** & **99.57**/98.78 & 99.30/98.40 \\ \hline Tubes & 39.45/85.88 & **97.06**/98.61 & 94.16/99.17 & 93.75/**99.44** \\ \hline Avg. & 73.16/88.07 & 93.84/97.45 & **94.94**/**97.80** & 91.82/97.54 \\ \hline \hline \end{tabular} Best results are highlighted in bold.  
\end{table}
Table 4: Effect of noise coefficient (\(\lambda\)) on image/pixel-level detection results (AUROC%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Image-Level**} & \multicolumn{2}{c}{**Pixel-Level**} \\ \hline Residual Attention module & \(\bigtriangledown\) & \(\bigtriangledown\) & \(\bigtriangledown\) & \(\bigtriangledown\) \\ \hline Bracket Black & **93.42** & 50.47 & **98.97** & 89.36 \\ \hline Bracket Brown & **93.14** & 75.32 & **93.10** & 80.36 \\ \hline Bracket White & **92.33** & 65.92 & **96.75** & 85.74 \\ \hline Connector & **100.00** & 62.38 & **98.95** & 94.99 \\ \hline Metal Plate & **99.57** & 88.61 & **98.78** & 96.69 \\ \hline Tubes & **94.16** & 57.87 & **99.17** & 84.93 \\ \hline Avg. & **95.44** & 66.76 & **97.62** & 88.68 \\ \hline \hline \end{tabular} Best results are highlighted in bold.  
\end{table}
Table 5: Effect of residual attention module on image/pixel-level detection results (AUROC%).  
combine the feature distribution of positive samples with a reconstruction approach to improve the anomaly detection performance of the model  
Conceptualization, S.D.; methodology, S.D.; validation, S.D. and Z.S.; data curation, S.D.; writing--original draft preparation, S.D.; writing--review and editing, Z.S.; visualization, S.D.; supervision, Z.S.; project administration, R.Z.; funding acquisition, R.Z. and J.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data used to support the findings of this study are included within the article. The authors declare no conflict of interest.