  Reconstruction-based methods are widely explored in industrial visual anomaly
detection. Such methods commonly require the model to well reconstruct the
normal patterns but fail in the anomalies, and thus the anomalies can be
detected by evaluating the reconstruction errors. However, in practice, it's
usually difficult to control the generalization boundary of the model. The
model with an overly strong generalization capability can even well reconstruct
the abnormal regions, making them less distinguishable, while the model with a
poor generalization capability can not reconstruct those changeable
high-frequency components in the normal regions, which ultimately leads to
false positives. To tackle the above issue, we propose a new reconstruction
network where we reconstruct the original RGB image from its gray value edges
(EdgRec). Specifically, this is achieved by an UNet-type denoising autoencoder
with skip connections. The input edge and skip connections can well preserve
the high-frequency information in the original image. Meanwhile, the proposed
restoration task can force the network to memorize the normal low-frequency and
color information. Besides, the denoising design can prevent the model from
directly copying the original high-frequent components. To evaluate the
anomalies, we further propose a new interpretable hand-crafted evaluation
function that considers both the color and gradient differences. Our method
achieves competitive results on the challenging benchmark MVTec AD (97.8\% for
detection and 97.7\% for localization, AUROC). In addition, we conduct
experiments on the MVTec 3D-AD dataset and show convincing results using RGB
images only. Our code will be available at
https://github.com/liutongkun/EdgRec.
