Single Image Rain Removal via Cascading Attention Aggregation Network on Challenging Weather Conditions

Image rain removal is extremely important since rain streaks can severely degrade the visibility which can decrease accuracy of many current computer vision algorithms. However, many deep-learning methods cannot adapt to the rain streak removal of different density labels, meanwhile retain the background details. To address this problem, we propose a novel image rain removal algorithm (CAAN) based on deep cascaded network and dual-channel attention mechanism. As contextual information is very crucial for rain removal, hence firstly the dilated convolution kernels of different scale branches are used to extract features of different rain streak sizes. Then we applied residual attention module (LMSRAM) to guide the learned features to be discriminative and beneficial. Specifically, a novel cascaded sub-network is developed to propagate information from lower to higher layers and decrease the loss of detailed information. Finally, the dual channel attention mechanism is used to make feature fusion efficiently. The experimental results demonstrate that the proposed method achieves the state-of-the-art results compared to the prevailing methods in terms of both quantitative metrics and visual quality. The source code of our proposed CAAN can download from the personal website. https://github.com/baiyihan/CAAN.