Name
Abstract | ||
|---|---|---|
Rain streaks severely hamper the visible performance of the outdoor surveillance videos, which becomes an attractive issue in recent computer vision research. Existing methods usually encode rain streaks into Gaussian Mixture Model (GMM). However, the limited number of Gaussian components in the GMM compromises the ability of the model in fitting real noise, such as sparse noise, which is exactly the characteristic of the rain streaks. In this paper, a novel model named Mixture Exponential Power Model (MEPM) is exploited. It sets multiple Laplace noise components and expands the representation capability for the sparse noise. Moreover, considering that the rain streaks in a video occur in different distances from the camera, we encode rain streaks into Multiscale Mixture Exponential Power Model. The model is optimized by expectation-maximization (EM) algorithm and Lagrange multiplier strategy. Experiments are implemented on synthetic and real rain videos and verify the superiority of the proposed method, compared with state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8682552 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Rain streaks, video, sparse noise, Mixture Exponential Power Model, Multi-scale | Exponential function,Pattern recognition,Laplace transform,Computer science,Streak,Matrix decomposition,Multiplier (economics),Gaussian,Artificial intelligence,Distortion,Mixture model | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofen Wang | 1 | 3 | 1.39 |
| Jun Chen | 2 | 187 | 27.66 |
| Zhen Han | 3 | 133 | 21.19 |
| Mingfu Xiong | 4 | 1 | 3.06 |
| Chao Liang | 5 | 160 | 9.58 |
| Qi Zheng | 6 | 60 | 11.78 |
| Hongqiang Wang | 7 | 313 | 40.65 |