Abstract: Due to the occlusion of the image caused by the different density of rain, it has always been a challenging task to remove the rain from the images. Nowadays, image de-raining algorithms based on deep learning have become the mainstream. However, most deep learning architectures are designed by stacking convolutional layers. For the rain removal task, the images have rain streaks in different sizes. To address this issue, A convolutional neural network based on multi-channel separation and integration for single image rain removal is designed. In the first step, the separable channels and the hierarchical connection between the convolutional layers form a multi-scale module. Finally, the outputs of different channels are integrated. The proposed module can expand the receptive field and explore the spatial information between feature maps, which extracts features better. In the second step, progressive networks are exploited to repeatedly calculate and excavate contextual information, which can be well related to global features. The proposed model is easy to be implemented and can be trained end-to-end. Extensive experiments on widely used datasets and self-built rainy images dataset for autonomous driving demonstrate that the proposed method has achieved significant improvements over the existing methods.

Key words: image rain removal, deep learning, convolutional neural network, multi-scale, channel separation and integration