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Entropy 2014, 16(6), 3302-3314; doi:10.3390/e16063302
Article

A Bayesian Probabilistic Framework for Rain Detection

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Received: 27 March 2014; in revised form: 27 May 2014 / Accepted: 9 June 2014 / Published: 17 June 2014
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Abstract: Heavy rain deteriorates the video quality of outdoor imaging equipments. In order to improve video clearness, image-based and sensor-based methods are adopted for rain detection. In earlier literature, image-based detection methods fall into spatio-based and temporal-based categories. In this paper, we propose a new image-based method by exploring spatio-temporal united constraints in a Bayesian framework. In our framework, rain temporal motion is assumed to be Pathological Motion (PM), which is more suitable to time-varying character of rain steaks. Temporal displaced frame discontinuity and spatial Gaussian mixture model are utilized in the whole framework. Iterated expectation maximization solving method is taken for Gaussian parameters estimation. Pixels state estimation is finished by an iterated optimization method in Bayesian probability formulation. The experimental results highlight the advantage of our method in rain detection.
Keywords: rain detection; Bayesian framework; spatio-temporal; expectation maximization rain detection; Bayesian framework; spatio-temporal; expectation maximization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Yao, C.; Wang, C.; Hong, L.; Cheng, Y. A Bayesian Probabilistic Framework for Rain Detection. Entropy 2014, 16, 3302-3314.

AMA Style

Yao C, Wang C, Hong L, Cheng Y. A Bayesian Probabilistic Framework for Rain Detection. Entropy. 2014; 16(6):3302-3314.

Chicago/Turabian Style

Yao, Chen; Wang, Ci; Hong, Lijuan; Cheng, Yunfei. 2014. "A Bayesian Probabilistic Framework for Rain Detection." Entropy 16, no. 6: 3302-3314.


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