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.

利用频率偏差进行图像恢复，以解决正常重建保真度和异常重建区分性之间的权衡问题，提出了一种新的自监督图像恢复任务——频率感知图像恢复（FAIR），在各种缺陷检测数据集上以更高的效率实现了最先进的性能。

FAIR: 频率感知的工业视觉异常检测图像恢复