Single Image Deraining by Fully Exploiting Contextual Information

Single-image deraining is challenging due to the lack of temporal information. Current methods based on deep neural networks have achieved good performance in this task. However, these methods are still less effective in handling complex rainy situations. In this paper, we propose Channel-attention-based Multi-scale Recurrent Residual Network (CMRRNET), which tries to fully exploit contextual information of rainy images from multiple aspects. First, we construct a hybrid feature extraction module, which consists of the dilated convolution block and the multi-scale convolution block, to fully obtain image feature information. Second, we adopt the residual channel attention mechanism which makes the network aware of the importance of different channels. Third, we introduce long short-term memory to extract the correlation information of the features between different stages. We conduct extensive experiments on both synthetic and real rainy images. Ablation studies and extensive comparisons with state-of-the-art methods demonstrate the effectiveness of our CMRRNET.