mlsanityData to 14 Oct 2026

FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection

arXiv 2309.07068 ↗First posted 13 Sep 2023cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 48 of its 66 references that are in the map. First posted on arXiv 13 Sep 2023.

Builds on

48 of 66 references.

References · 48

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36 more
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