{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fair-frequency-aware-image-restoration-for","title":"FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection","arxiv_id":"2309.07068","date":"2023-09-13","proceeding":null,"authors":["Tongkun Liu","Bing Li","Xiao Du","Bingke Jiang","Leqi Geng","Feiyang Wang","Zhuo Zhao"],"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.","url_abs":"https://arxiv.org/abs/2309.07068v1","url_pdf":"https://arxiv.org/pdf/2309.07068v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fair-frequency-aware-image-restoration-for","repo_url":"https://github.com/liutongkun/fair","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"defect-detection","task_name":"Defect Detection"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"image-restoration","task_name":"Image Restoration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"FAIR","rank_in_archive_order":55,"of":148,"metrics":{"Detection AUROC":"98.6","Segmentation AUPRO":"94.0","Segmentation AUROC":"98.2"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-visa","task":"Anomaly Detection","dataset":"VisA","model":"FAIRnoDTD","rank_in_archive_order":17,"of":50,"metrics":{"Detection AUROC":"97.1","Segmentation AUPRO (until 30% FPR)":"91.2","Segmentation AUROC":"98.7"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2309.07068","atlas_url":"https://app.syntology.ai/?focus=2309.07068","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}