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

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arxiv 2309.07068 v1 pith:G6HQSYRK submitted 2023-09-13 cs.CV

Tongkun Liu , Bing Li , Xiao Du , Bingke Jiang , Leqi Geng , Feiyang Wang , Zhuo Zhao This is my paper · ORCID
classification cs.CV
keywords fairimagereconstructiondetectionnormalrestorationabnormalanomaly
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SALAD -- Semantics-Aware Logical Anomaly Detection

    cs.CV 2025-09 conditional novelty 7.0 of 10

    SALAD trains a network on automatically extracted 'composition maps' of object parts and detects logical anomalies (missing/extra parts) with a 96.1% AUROC on MVTec LOCO, the best published result.

  2. Unlocking the Potential of Reverse Distillation for Anomaly Detection

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A reverse-distillation anomaly detector trained with a frozen expert reference and attention-gated skip connections improves localization on MVTec AD, MPDD, BTAD, and VisA.

  3. DACA-Net: A Degradation-Aware Conditional Diffusion Network for Underwater Image Enhancement

    cs.CV 2025-07 reject novelty 5.0 of 10

    A degradation score trained on PSNR conditions a Swin UNet diffusion model for underwater enhancement, reporting top UIEB and LSUI scores, but the reference-free inference mechanism is unspecified.

  4. CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP

    cs.CV 2024-12 conditional novelty 4.0 of 10

    A CLIP-based few-shot anomaly classifier with adapters and a cross-modal Anomaly Descriptor reports state-of-the-art VisA results and competitive, not always best, MVTEC-AD results.

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