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Recurrent Attention Dense Network for Single Image De-raining
2020
IEEE Access
The problem of single image rain removal has attracted tremendous attention as the blurry images caused by rain streaks can degrade the performance of many computer vision algorithms. Although deep learning based de-raining methods have achieved a significant success, there are still unresolved issues in terms of the performance. In this work, we propose a novel recurrent attention dense network (RADN) for single image de-raining. In RADN, a region-level attention module is first utilized to
doi:10.1109/access.2020.3003126
fatcat:rme4a3u3fnao5iyid55vihoqf4