Recurrent Attention Dense Network for Single Image De-Raining

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 identify rain streaks regions for the subsequent removal task. As rain streaks have different sizes and shapes, a modified densely connected convolutional network (DenseNet) with dilation convolutions and reduced channels is developed for an effective feature representation. The rain streaks are removed stage by stage and a Gate Recurrent Unit (GRU) is incorporated to deliver useful information from previous stages to later stages for a better performance. Qualitative and quantitative evaluations on both synthetic and real-world datasets demonstrate that the proposed approach can achieve a remarkable performance in comparison with the state-of-the-art methods for single image rain removal.