Restoration of Images Taken Through a Dirty Window Using Optics-Guided Transformer

Taking photographs through windows is an inevitable scenario in the real world, but glass windows are not ideally clean in most cases. Although there exists various raindrop removal methods, the occlusion of dirt, as another dirty window case, has not been well valued. The vital reasons include i) the limitation of the optical imaging model proposed in previous methods, and ii) the shortage of a practical dataset for sufficient types of dirty glass windows. To fill this research gap, in this paper, we first propose a general optical imaging model that fits widely used dirty window cases. Following this, training and testing synthetic datasets are generated, and real-world dirty window data are collected to evaluate the effectiveness of our imaging model and synthetic data. For the methodology part, we propose an optics-guided Transformer network to solve this special image restoration problem, i.e., the dirt removal for images taken through a dirty window. Experimental results demonstrate that our imaging model is effective and robust. Our proposed network leads to higher performance than existing methods on both synthetic and real-world dirty window images. Code and data are available at https://github.com/Zongliang-Wu/ReDNet.