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-tofine 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 realworld rain images show that our self-refining DSEN yields the top performance.
Liu et al. REF proposed a novel symmetry enhanced network to explicitly remove the tilted rain streaks from rain images.
Self-Refining Deep Symmetry Enhanced Network for Rain Removal
2019 IEEE International Conference on Image Processing (ICIP)
2019 IEEE International Conference on Image Processing (ICIP)
Mathematics Environmental Science Computer Science Engineering