Lightweight Semi-supervised Network for Single Image Rain Removal

Deep learning technologies have shown their advantages in Single Image Rain Removal (SIRR) tasks. However, the derained results of most methods are limited to some challenges. First, due to the lack of real-world rainy/clean image pairs, many methods seriously rely on the labeled synthetic training images and will not effectively remove complex rain streaks in real-world scenarios. Second, most existing SIRR models require high computing power, which considerably limits their real-world applications. To address these issues, we propose a Lightweight Semi-supervised Network (LSNet) for SIRR. Our LSNet utilizes a compact semi-supervised framework to improve generalization ability in real-world rainy images removal. Meanwhile, in our semi-supervised framework, we also design a cascaded sub-network, which progressively removes complex rain streaks via a multi-stage manner. Specially, the multi-stage manner is based on a series of cascaded blocks, where we conduct recursive learning strategy to reduce model parameters. Extensive experimental results demonstrate that our method achieves comparable performance to the state-of-the-arts while has fewer parameters.