Name
Abstract | ||
|---|---|---|
Reducing noise in a video sequence is of vital important in many real-world applications. One popular method is block matching collaborative filtering. However, the main drawback of this method is that noise standard deviation for the whole video sequence is known in advance. In this paper, we present a tensor based denoising framework that considers 3D patches instead of 2D patches. By collecting the similar 3D patches non-locally, we employ the low-rank tensor decomposition for collaborative filtering. Since we specify the non-informative prior over the noise precision parameter, the noise variance can be inferred automatically from observed video data. Therefore, our method is more practical, which does not require knowing the noise variance. The experimental on video denoising demonstrates the effectiveness of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1117/12.2268435 | Proceedings of SPIE |
Keywords | Field | DocType |
Video denoising,low rank tensor decomposition,tensor rank,collaborative filtering | Noise reduction,Computer vision,Collaborative filtering,Pattern recognition,Tensor,Tensor rank,Artificial intelligence,Standard deviation,Video denoising,Mathematics,Tensor decomposition | Conference |
Volume | ISSN | Citations |
10341 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gui Lihua | 1 | 5 | 1.10 |
| Gaochao Cui | 2 | 3 | 3.79 |
| Qibin Zhao | 3 | 905 | 68.65 |
| Dongsheng Wang | 4 | 0 | 0.34 |
| Andrzej Cichocki | 5 | 5228 | 508.42 |
| Jianting Cao | 6 | 194 | 34.47 |