  Anomaly detection has a wide range of applications and is especially
important in industrial quality inspection. Currently, many top-performing
anomaly-detection models rely on feature-embedding methods. However, these
methods do not perform well on datasets with large variations in object
locations. Reconstruction-based methods use reconstruction errors to detect
anomalies without considering positional differences between samples. In this
study, a reconstruction-based method using the noise-to-norm paradigm is
proposed, which avoids the invariant reconstruction of anomalous regions. Our
reconstruction network is based on M-net and incorporates multiscale fusion and
residual attention modules to enable end-to-end anomaly detection and
localization. Experiments demonstrate that the method is effective in
reconstructing anomalous regions into normal patterns and achieving accurate
anomaly detection and localization. On the MPDD and VisA datasets, our proposed
method achieved more competitive results than the latest methods, and it set a
new state-of-the-art standard on the MPDD dataset.
