Rain-component-aware capsule-GAN for single image de-raining
作者：
Highlights：
• The performance of de-raining models benefits from the joint learning of rain removal and content recovery.
• Rain components are better learned with well-designed rain aware network.
• Relationship between objects of the whole image plays an important part in rain identification.
摘要
•The performance of de-raining models benefits from the joint learning of rain removal and content recovery.•Rain components are better learned with well-designed rain aware network.•Relationship between objects of the whole image plays an important part in rain identification.
论文关键词：De-raining,Capsule,Generative adversarial network,Rain-component-aware network
论文评审过程：Received 9 December 2020, Revised 24 September 2021, Accepted 18 October 2021, Available online 19 October 2021, Version of Record 25 October 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108377
