An Image Enhancement method for Anomaly Detection
Jingxiang Duan, Junjia Zhang, Bo Wang, Qiang Ling · 2024
Visual anomaly detection is a critical task of various computer vision applications. In some challenging situations, the given anomalous image may contain large artificial occlusions which should not be detected as anomalous regions, but may be easily misjudged by common anomaly detection methods. To resolve this issue, we propose an image enhancement method through generating masks corresponding to large occlusions and replacing the masked region with normal background texture. In the proposed method, the inpainting algorithm is specifically designed to distinguish anomaly regions from normal background and ensure the quality of generated texture. Experiments show that our image enhancement method can effectively improves the anomaly detection performance.