Name
Abstract | ||
|---|---|---|
We develop a novel joint-view Kalman filter for causal reconstruction of compressed-sensed multiview videos. Compressed-sensed multiview video frames are initially reconstructed individually via l1-norm minimization. Then, ajoint-view state transition model is established for each pair of neighboring views using motion or motion-disparity field estimates. Experimental results demonstrate significantly improved reconstruction quality compared to conventional CS reconstruction and independent-view (single-view) motion-compensated Kalman filtering. |
Year | Venue | Field |
|---|---|---|
2016 | ICASSP | Computer vision,Pattern recognition,Computer science,Joint reconstruction,Kalman filter,Minification,Artificial intelligence,Compressed sensing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Liu | 1 | 60 | 5.05 |
| Shubham Chamadia | 2 | 3 | 3.78 |
| Dimitris Pados | 3 | 208 | 26.49 |