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

• URL: http://arxiv.org/abs/2309.07068v1
• Date: Wed, 13 Sep 2023 16:28:43 GMT
• Title: FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection
• Authors: Tongkun Liu, Bing Li, Xiao Du, Bingke Jiang, Leqi Geng, Feiyang Wang, Zhuo Zhao
• Abstract summary: Frequency-aware Image Restoration (FAIR) is a novel self-supervised image restoration task that restores images from their high-frequency components. FAIR achieves state-of-the-art performance with higher efficiency on various defect detection datasets.
• Score: 4.705841907301398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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