  Single image deraining is an urgent task because the degraded rainy image
makes many computer vision systems fail to work, such as video surveillance and
autonomous driving.
  So, deraining becomes important and an effective deraining algorithm is
needed.
  In this paper, we propose a novel network based on physical model guided
learning for single image deraining, which consists of three sub-networks: rain
streaks network, rain-free network, and guide-learning network.
  The concatenation of rain streaks and rain-free image that are estimated by
rain streaks network, rain-free network, respectively, is input to the
guide-learning network to guide further learning and the direct sum of the two
estimated images is constrained with the input rainy image based on the
physical model of rainy image.
  Moreover, we further develop the Multi-Scale Residual Block (MSRB) to better
utilize multi-scale information and it is proved to boost the deraining
performance.
  Quantitative and qualitative experimental results demonstrate that the
proposed method outperforms the state-of-the-art deraining methods.
  The source code will be available at
\url{https://supercong94.wixsite.com/supercong94}.
