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 feat...
