Given the rarity and diversity of anomalies, comprehensive anomaly type collection is infeasible. Researchers thus rely on unsupervised learning techniques trained solely on normal samples. Recently, S-T framework-based methods have shown promising results. However, they struggle with detecting structural anomalies resembling normal samples. To tackle this issue, we introduce a self-supervised reconstruction module in the student network’s final layer, which masks the central part of the receptive field and leverages contextual information to predict the masked values. This design encourages the model to learn and utilize surrounding contextual information, enabling a deeper understanding of the intrinsic structure of normal samples and, consequently, improving the detection of potential anomalies. We assess the effectiveness of our approach on the MVTECAD and MVTEC Loco AD datasets. Our experimental results demonstrate that our method achieves state-of-the-art average performance on both datasets.