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

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.

Data and Resources

Cite this as

Tongkun Liua, Bing Lia, Xiao Dua, Bingke Jianga, Leqi Genga, Feiyang Wang, Zhuo Zhaoa (2024). Dataset: FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection. https://doi.org/10.57702/ohq0cmal

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2309.07068
Author Tongkun Liua
More Authors
Bing Lia
Xiao Dua
Bingke Jianga
Leqi Genga
Feiyang Wang
Zhuo Zhaoa
Homepage https://github.com/liutongkun/FAIR