Industrial anomaly detection is dedicated to identifying and locating regions that deviate from the standard appearance. The prevailing approach achieves unsupervised anomaly detection through the reconstruction of images using autoencoders. Due to the simplistic structure of some abnormal regions, the autoencoder can effectively reconstruct these areas, consequently diminishing the model’s anomaly detection capabilities. To address this issue, this paper transforms the reconstruction task into the inpainting-filling-reconstruction task to increase the reconstruction error between abnormal samples and normal samples. The masked regions inpainted by the filling network are used to fill in the input image, thereby achieving an effect similar to masking. Unlike typical masking processes, this approach retains partial authentic information in the input image, rendering it partially visible. This is beneficial for the reconstruction network to repair the masked areas. Due to the consistent structure between the masked region inpainted by the filling network and the normal region, the filled abnormal regions display a complex structure that has not been learned, making it difficult for the reconstruction network to reconstruct the abnormal regions. Experimental results indicate that our method performs better than other methods on both the MVTec AD dataset and the MVTec LOCO AD dataset.