Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection

Autoencoders have emerged as popular methods for unsupervised anomaly detection. Autoencoders trained on the normal data are expected to reconstruct only the normal features, allowing anomaly detection by thresholding reconstruction errors. However, in practice, autoencoders fail to model small detail and yield blurry reconstructions, which makes anomaly detection challenging. Moreover, there is objective mismatching that models are trained to minimize total reconstruction errors while we expect a small deviation on normal pixels and large deviation on anomalous pixels. To tackle these two issues, we propose the iterative image inpainting method that reconstructs partial regions in an adaptive inpainting mask matrix. This method constructs inpainting masks from the anomaly score of structural similarity. Overlaying inpainting mask on images, each pixel is bypassed or reconstructed based on the anomaly score, enhancing reconstruction quality. The iterative update of inpainted images and masks by turns purifies the anomaly score directly and follows the expected objective at test time. We evaluated the proposed method using the MVTec Anomaly Detection dataset. Our method outperforms previous state-of-the-art in several categories and showed remarkable improvement in high-frequency textures.