  Image reconstruction-based anomaly detection models are widely explored in
industrial visual inspection. However, existing models usually suffer from the
trade-off between normal reconstruction fidelity and abnormal reconstruction
distinguishability, which damages the performance. In this paper, we find that
the above trade-off can be better mitigated by leveraging the distinct
frequency biases between normal and abnormal reconstruction errors. To this
end, we propose Frequency-aware Image Restoration (FAIR), a novel
self-supervised image restoration task that restores images from their
high-frequency components. It enables precise reconstruction of normal patterns
while mitigating unfavorable generalization to anomalies. Using only a simple
vanilla UNet, FAIR achieves state-of-the-art performance with higher efficiency
on various defect detection datasets. Code: https://github.com/liutongkun/FAIR.
