Multi-receptive Field Aggregation Network for single image deraining

• Published in: Journal of visual communication and image representation 2022-04, Vol.84, p.103469, Article 103469
• Main Authors: Liang, Songliang, Meng, Xiaozhe, Su, Zhuo, Zhou, Fan
• Format: Article
• Language: English
• Subjects: Attention mechanism; Dilated convolution; Image deraining
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2022.103469

Image deraining is a significant problem that ensures the visual quality of images to prompt computer vision systems. However, due to the insufficiency of captured rain streaks features and global information, current image deraining methods often face the issues of rain streaks remaining and image blurring. In this paper, we propose a Multi-receptive Field Aggregation Network (MRFAN) to restore a cleaner rain-free image. Specifically, we construct a Multi-receptive Field Feature Extraction Block (MFEB) to capture rain features with different receptive fields. In MFEB, we design a Self-supervised Block (SSB) and an Aggregation Block (AGB). SSB can make the network adaptively focus on the critical rain features and rain-covered areas. AGB effectively aggregates and redistributes the multi-scale features to help the network simulate rain streaks better. Experiments show that our method achieves better results on both synthetic datasets and real-world rainy images. •Propose a Multi-receptive Field Aggregation Network for single image deraining.•Design a Multi-receptive Field Feature Extraction Block to extract rain features.•Design a Self-supervised Block to integrate the channel and spatial information.•Present an Aggregation Block to fuse multi-features adaptively.