MARA-Net: Single Image Deraining Network with Multi-level connections and Adaptive Regional Attentions

Removing rain streaks from single images is an important problem in various computer vision tasks because rain streaks can degrade outdoor images and reduce their visibility. While recent convolutional neural network-based deraining models have succeeded in capturing rain streaks effectively, difficulties in recovering the details in rain-free images still remain. In this paper, we present a multi-level connection and adaptive regional attention network (MARA-Net) to properly restore the original background textures in rainy images. The first main idea is a multi-level connection design that repeatedly connects multi-level features of the encoder network to the decoder network. Multi-level connections encourage the decoding process to use the feature information of all levels. Channel attention is considered in multi-level connections to learn which level of features is important in the decoding process of the current level. The second main idea is a wide regional non-local block (WRNL). As rain streaks primarily exhibit a vertical distribution, we divide the grid of the image into horizontally-wide patches and apply a non-local operation to each region to explore the rich rain-free background information. Experimental results on both synthetic and real-world rainy datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art models. Furthermore, the results of the joint deraining and segmentation experiment prove that our model contributes effectively to other vision tasks

Self-Refining Deep Symmetry Enhanced Network for Rain Removal

Rain removal aims to remove the rain streaks on rain images. The state-of-the-art methods are mostly based on Convolutional Neural Network~(CNN). However, as CNN is not equivariant to object rotation, these methods are unsuitable for dealing with the tilted rain streaks. To tackle this problem, we propose Deep Symmetry Enhanced Network~(DSEN) that is able to explicitly extract the rotation equivariant features from rain images. In addition, we design a self-refining mechanism to remove the accumulated rain streaks in a coarse-to-fine manner. This mechanism reuses DSEN with a novel information link which passes the gradient flow to the higher stages. Extensive experiments on both synthetic and real-world rain images show that our self-refining DSEN yields the top performance.Comment: Accepted by ICIP 19. Corresponding and contact author: Hanrong Y