A Bayesian Probabilistic Framework for Rain Detection

Entropy

Chen Yao¹  Ci Wang²  Lijuan Hong¹ 

[1] The Third Research Institute of Ministry of Public Security, No. 76 Yueyang Road, Shanghai, China; E-Mails:;Department of Electronic Engineering, School of Electronic, Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai, China

关键词: rain detection;    Bayesian framework;    spatio-temporal;    expectation maximization;   

DOI  :  10.3390/e16063302

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland