Anomaly Detection and Segmentation Based on Defect Repaired Image Resynthesis
Anomaly detection is a challenging task in data analysis, especially when it comes to unsupervised pixel-level segmentation of anomalies in images. In this paper, we present a novel multi-stage defect repaired image resynthesis framework for the detection and segmentation of anomalies in images. In contrast to the existing reconstruction-based approaches, our reconstruction is free from artifacts caused by defective regions so that the defects can be identified from the residual map between input samples and their resynthesized defect-eliminated outputs. Our method outperforms the state-of-art benchmarks in most categories using the publicly available MVTec dataset. Besides, the method also demonstrates an excellent capability of repairing defects in abnormal samples.