  A deraining network can be interpreted as a conditional generator that aims
at removing rain streaks from image. Most existing image deraining methods
ignore model errors caused by uncertainty that reduces embedding quality.
Unlike existing image deraining methods that embed low-quality features into
the model directly, we replace low-quality features by latent high-quality
features. The spirit of closed-loop feedback in the automatic control field is
borrowed to obtain latent high-quality features. A new method for error
detection and feature compensation is proposed to address model errors.
Extensive experiments on benchmark datasets as well as specific real datasets
demonstrate that the proposed method outperforms recent state-of-the-art
methods. Code is available at: \\ https://github.com/LI-Hao-SJTU/DerainRLNet
