  Image-based systems have gained popularity owing to their capacity to provide
rich manufacturing status information, low implementation costs and high
acquisition rates. However, the complexity of the image background and various
anomaly patterns pose new challenges to existing matrix decomposition methods,
which are inadequate for modeling requirements. Moreover, the uncertainty of
the anomaly can cause anomaly contamination problems, making the designed model
and method highly susceptible to external disturbances. To address these
challenges, we propose a two-stage strategy anomaly detection method that
detects anomalies by identifying suspected patches (Ano-SuPs). Specifically, we
propose to detect the patches with anomalies by reconstructing the input image
twice: the first step is to obtain a set of normal patches by removing those
suspected patches, and the second step is to use those normal patches to refine
the identification of the patches with anomalies. To demonstrate its
effectiveness, we evaluate the proposed method systematically through
simulation experiments and case studies. We further identified the key
parameters and designed steps that impact the model's performance and
efficiency.
