A Bayesian Probabilistic Framework for Rain Detection
AbstractHeavy 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Yao, C.; Wang, C.; Hong, L.; Cheng, Y. A Bayesian Probabilistic Framework for Rain Detection. Entropy 2014, 16, 3302-3314.
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.