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.

Image Deblurring of Video Surveillance System in Rainy Environment

Masini, N
2020-01-01

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.
2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/156471
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