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PADiff: Reconstruction From Patch to Pixel With Normality-Guided Diffusion Model for Unsupervised Anomaly

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|June 17, 2025

Summary

This summary is machine-generated.

This study introduces PADiff, a novel framework for anomaly localization (AL) in manufacturing. PADiff enhances diffusion models by guiding reconstruction with normal counterparts, achieving state-of-the-art performance on benchmark datasets.

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Area of Science:

Manufacturing
Computer Vision
Artificial Intelligence

Background:

Anomaly localization (AL) is crucial but challenging in manufacturing.
Diffusion models show promise for AL by reconstructing anomalies as normal, but struggle with deviations from Gaussian distributions.
Existing diffusion models have suboptimal performance in AL due to generalization capabilities.

Purpose of the Study:

To present a novel framework, PADiff, for anomaly localization using diffusion models.
To improve the reconstruction of abnormal regions to normal patterns within anomaly images.
To enhance the data efficiency and performance of diffusion models in anomaly detection.

Main Methods:

PADiff guides diffusion model reconstruction using a normal counterpart derived from a patch-substitution strategy.
A normal patch memory bank is constructed from training samples to find and substitute anomalous patches.
Patch-wise training, reconstruction, and positional embedding are employed for data efficiency and improved representation.

Main Results:

PADiff demonstrates state-of-the-art (SOTA) performance on MVTec-AD, VisA, and BTAD anomaly detection datasets.
The proposed patch-substitution strategy effectively generates high-quality normal counterparts for guidance.
Patch-wise processing and positional embeddings improve the model's ability to handle localized anomalies.

Conclusions:

PADiff offers a significant advancement in anomaly localization by effectively guiding diffusion models.
The method addresses limitations of standard diffusion models in handling anomalous data distributions.
PADiff provides a robust and data-efficient solution for anomaly detection in manufacturing settings.