Unsupervised Anomaly Segmentation Via Multilevel Image Reconstruction and Adaptive Attention-Level Transition
Abstract
In product inspection scenarios, the objects have different characteristics of textures and different potential anomalies. Still, traditional methods cannot precisely segment structural anomalies and achieve optimal performance on all object categories. This article introduces a flexible multilevel image reconstruction (MLIR) framework for the unsupervised anomaly segmentation task. The MLIR integrates multiple generators with different downsampling ratios to serve as multiple reconstruction levels and leverage an adjustable perceptual similarity measurement to obtain the featurewise reconstruction errors, overcoming the challenge of detecting multiscale anomalies. Leveraging embeddings with different receptive fields for reconstruction, generators perform differently on the same target. Therefore, we depend on the reconstruction uncertainty to propose an adaptive attention-level transition (ALT) strategy that optimizes the detection performance automatically according to the target's category. ALT adjusts the weights of both reconstruction levels and feature measurement scales simultaneously to utilize features at a consistent level for reconstruction and anomaly detection (AD). This strategy remarkably improves the system's adaptivity for different applications. Moreover, we propose reducing the signal-to-noise ratio (SNR) of the images by adding pepper noise so that the reconstruction process is framed as an image denoising task, which improves feature extraction and image restoration robustness. We conduct extensive experiments on the MVTec AD dataset and demonstrate that the proposed method performs satisfactorily to different categories and achieves the state-of-the-art (SOTA) performance.
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(2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3139417
Liu, M., Jiao, Y., Lü, J., & Chen, H. (2023) · doi:10.36227/techrxiv.24330880
Orthogonal Latent Compression for Streaming Anomaly Detection in Industrial Vision
Gao, H., Luo, H., Shen, F., & Zhang, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78189-6_7
Memory-Guided Hierarchical Feature Reconstruction for Multi-class Unsupervised Anomaly Detection
Huang, K., Zhou, S., Hu, W., Gao, Y., Pan, F., & Jiang, X. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4276-2_16
An Improved NAMLab Algorithm by Using the Newest Color Difference Formula CIEDE2000
Zheng, Y., Huang, J., & Sarem, M. (2025) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd67701.2025.11198009
UAD-X: A Universal Anomaly Detection Framework for X-Ray Inspection of Electronic Components
Bai, H., Wang, J., Li, G., Li, X., Zhang, X., & Yang, X. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3670570
Hsu, H.-M., Chang, K.-L., Chang, W.-C., Lee, C.-H., Hsueh, H.-W., Tseng, C.-W., & Hsu, S.-H. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-17900-5
Semi-supervised deep learning for robust anomaly detection in smart manufacturing systems
Le, T. (2024) · theses.fr (ABES) · doi:10.70675/19d23228zf030z4df7za3abz596d36577818
Li, B., Ling, Y., & Zexian, W. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae5ec8
Boundary-Aware Structural-Semantic Pseudo Anomaly Learning for Industrial Anomaly DetectionPreprint
Guo, C., Jiang, H., Ying, N., & Shen, L. (2026) · Research Square · doi:10.21203/rs.3.rs-9925157/v1
- Authors
- Yan, Y., Wang, D., Zhou, G., & Chen, Q.
- Affiliations
- Tongji University (CN)
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2021
- Volume
- 70
- Pages
- 1-12
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W3193992273
- Cited by
- 60
- References
- 52
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 15 · 2023: 17 · 2022: 3 · 2021: 9 · 2020: 1
- FWCI
- 5.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Funders
- National Natural Science Foundation of China (61733013, 62073245)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- product inspection, reconstruction uncertainty, image denoising, MVTec AD, generative models
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