  A common study area in anomaly identification is industrial images anomaly
detection based on texture background. The interference of texture images and
the minuteness of texture anomalies are the main reasons why many existing
models fail to detect anomalies. We propose a strategy for anomaly detection
that combines dictionary learning and normalizing flow based on the
aforementioned questions. The two-stage anomaly detection approach already in
use is enhanced by our method. In order to improve baseline method, this
research add normalizing flow in representation learning and combines deep
learning and dictionary learning. Improved algorithms have exceeded 95$\%$
detection accuracy on all MVTec AD texture type data after experimental
validation. It shows strong robustness. The baseline method's detection
accuracy for the Carpet data was 67.9%. The article was upgraded, raising the
detection accuracy to 99.7%.
