  Unsupervised anomaly detection using only normal samples is of great
significance for quality inspection in industrial manufacturing. Although
existing reconstruction-based methods have achieved promising results, they
still face two problems: poor distinguishable information in image
reconstruction and well abnormal regeneration caused by model
over-generalization ability. To overcome the above issues, we convert the image
reconstruction into a combination of parallel feature restorations and propose
a multi-feature reconstruction network, MFRNet, using crossed-mask restoration
in this paper. Specifically, a multi-scale feature aggregator is first
developed to generate more discriminative hierarchical representations of the
input images from a pre-trained model. Subsequently, a crossed-mask generator
is adopted to randomly cover the extracted feature map, followed by a
restoration network based on the transformer structure for high-quality repair
of the missing regions. Finally, a hybrid loss is equipped to guide model
training and anomaly estimation, which gives consideration to both the pixel
and structural similarity. Extensive experiments show that our method is highly
competitive with or significantly outperforms other state-of-the-arts on four
public available datasets and one self-made dataset.
