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Unsupervised pixel-level defective region segmentation is an important taskin image-based anomaly detection for various industrial applications. Thestate-of-the-art methods have their own advantages and limitations:matrix-decomposition-based methods are robust to noise but lack complexbackground image modeling capability; representation-based methods are good atdefective region localization but lack accuracy in defective region shapecontour extraction; reconstruction-based methods detected defective regionmatch well with the ground truth defective region shape contour but are noisy.To combine the best of both worlds, we present an unsupervised patchautoencoder based deep image decomposition (PAEDID) method for defective regionsegmentation. In the training stage, we learn the common background as a deepimage prior by a patch autoencoder (PAE) network. In the inference stage, weformulate anomaly detection as an image decomposition problem with the deepimage prior and domain-specific regularizations. By adopting the proposedapproach, the defective regions in the image can be accurately extracted in anunsupervised fashion. We demonstrate the effectiveness of the PAEDID method insimulation studies and an industrial dataset in the case study.

PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation