MOONLIT: momentum-contrast and large-kernel for multi-fine-grained deraining

Abstract

Deep learning-based methods have achieved excellent performance in image-deraining tasks. Unfortunately, most existing deraining methods incorrectly assume a uniform rain streak distribution and a fixed fine-grained level. And this uncertainty of rain streaks will result in the model not being competent at repairing all fine-grained rain streaks. In addition, some existing convolution-based methods extend the receptive field mainly by stacking convolution kernels, which frequently results in inaccurate feature extraction. In this work, we propose momentum-contrast and large-kernel for multi-fine-grained deraining network (MOONLIT). To address the problem that the model is not competent at all fine-grained levels, we use the unsupervised dictionary contrastive learning method to treat different fine-grained rainy images as different degradation tasks. Then, to address the problem of inaccurate feature extraction, we carefully constructed a restoration network based on large-kernel convolution with a larger and more accurate receptive field. In addition, we designed a data enhancement method to weaken features other than rain streaks in order to be better classified for different degradation tasks. Extensive experiments on synthetic and real-world deraining datasets show that the proposed method MOONLIT achieves the state-of-the-art performance on some datasets. Code is available at https://github.com/awhitewhale/moonlit.