Learn to Represent and Suppress Smears With Imaging Mechanism Guided Neural Network

In charge-coupled device digital imaging, smears occur when light from a scene affects the detector during noninstantaneous charge transfer, creating bright lines across the image that hinder astronomical observations. Existing image processing methods struggle to remove these smears without influencing follow-up process. The lack of labeled data further complicates the development of deep learning-based approaches in this area. To address these challenges, we propose an imaging-model-based method to represent smears and guide supervised networks in learning smear suppression. Leveraging the imaging mechanism, the proposed network (IMGNet) identifies the charge transfer direction and smeared regions to produce clean images. Trained on simulated data, the method outperforms traditional, supervised, and unsupervised baselines. By explicitly representing smears, the network effectively mitigates the impact of domain gaps with authentic data. Experiments on downstream tasks, such as detection and registration, further demonstrate the superior performance of the proposed method.