Abstract: In view of the fact that most traditional video rain removal algorithms only focus on removing rain marks and are trained only on synthetic data， ignoring more complex degradation factors such as rain accumulation， occlusion， and prior knowledge in real data. In this paper， we propose a two-stage video deraining algorithm that combines synthetic and real videos. The first stage algorithm performs a reverse recovery process under the guidance of the proposed rain removal model Initial-DerainNet. Continuous rain frames containing degradation factors are input into the network and physical prior knowledge is integrated to obtain an initial estimated rain-free frame. The second stage uses adversarial learning to refine the results， that is， to restore the overall color， illumination distribution， etc. of the initially estimated rain-free frame to obtain a more accurate rain-free frame. Experimental results show that the PSNR value of this algorithm reaches 35.22 dB and the SSIM value reaches 0.9596 on the synthetic rain removal data set RainSyntheticDataset100， which is better than benchmark rain removal algorithms such as JORDER， DetailNet， SpacNN， SE， J4Rnet and FastDeRain. On the real rain video test set， the algorithm in this paper can achieve PNSR values of more than 30 dB on rain videos of different dimensions， which is better than other rain removal algorithms in terms of subjective visual effect and data metrics， and can effectively improve the quality of rainy day videos.

Key words:  , video deraining algorithm； physics-based restoration； generative adversarial network； degradation factors