@Article{cmc.2020.011044,
AUTHOR = {Jinxing Niu, Yajie Jiang, Yayun Fu, Tao Zhang, Nicola Masini},
TITLE = {Image Deblurring of Video Surveillance System in Rainy  Environment},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {807--816},
URL = {http://www.techscience.com/cmc/v65n1/39596},
ISSN = {1546-2226},
ABSTRACT = {Video surveillance system is used in various fields such as transportation and 
social life. The bad weather can lead to the degradation of the video surveillance image 
quality. In rainy environment, the raindrops and the background are mixed, which lead to 
make the image degradation, so the removal of the raindrops has great significance for 
image restoration. In this article, after analyzing the inter-frame difference method in 
detecting and removing raindrops, a background difference method is proposed based on 
Gaussian model. In this method, the raindrop is regarded as a moving object relative to 
the background. The principle and procedure of the method are given to detect and 
remove raindrops. The parameters of the single Gaussian background model are studied 
in this article. The important parameter of the learning rate of Gaussian model is explored 
in order to better detection and removal of raindrops. Experiment shows that the results 
of removal of raindrops by using the proposed algorithm are better than that by using the 
inter-frame difference method. The image processing effect is the best when the learning 
rate is 0.6. The research results can provide technical reference for similar research on 
eliminating the influence of rainy weather.},
DOI = {10.32604/cmc.2020.011044}
}