Contrastive learning-based generative network for single image deraining
Yongqiang Du, Zilei Shen, Yining Qiu, Songnan Chen · Journal of Electronic Imaging · 2022
The crux of image deraining stems from the challenge of recognizing the diverse rain patterns within the rainy image. Most methods for image deraining remain visible rain residuals in the restored image, which suffers from insufficient modeling of rain streaks. In this work, we propose contrastive learning-based generative network (CLGNet), which follows a coarse-to-fine framework. In the coarse phase, our CLGNet employs the hierarchical encoder–decoder structure to remove obvious rain patterns, and first generates the coarse background image. Then, we introduce a well-designed multiscale feature aggregation module in the refining phase to extract and integrate global information dependencies from different scales. In additon, to facilitate the intra-stage and cross-stage information interaction, we propose the intra-stage feature fusion module and the cross-stage feature fusion module to encode broad contextual information. More importantly, we propose an innovative contrastive learning strategy and apply it to each stage of our proposed CLGNet to enhance the decoupling ability of the encoder and help the model recognize complex rain patterns. Extensive experiments on five benchmark datasets demonstrate the superiority of our proposed CLGNet than other state-of-the-art methods for single image deraining on both the visual quality and quantitative evaluation.