Name
Abstract | ||
|---|---|---|
We consider the problem of reconstructing frames from a video which has been compressed using the video compressive sensing (VCS) method. In VCS data, each frame comes from first subsampling the original video data in space and then averaging the subsampled sequence in time. This results in a large linear system of equations whose inversion is ill-posed. We introduce a convex regularizer to invert the system, where the spatial component is regularized by the total variation seminorm, and the temporal component is regularized by enforcing sparsity on the difference between the spatial gradients of each frame. Since the regularizers are L-1 -like norms, the model can be written in the form of an easy-to-solve saddle point problem. The saddle point problem is solved by the primaldual algorithm, whose implementation calls for nearly pointwise operations (i.e., no direct linear inversion) and has a simple parallel version. Results show that our model decompresses videos more accurately than other popular models, with PSNR gains of several dB. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1137/140977400 | SIAM JOURNAL ON IMAGING SCIENCES |
Keywords | Field | DocType |
video decompression,compressive sensing,total variation,temporal regularization | Space time,Mathematical optimization,Saddle point,System of linear equations,Inversion (meteorology),Regular polygon,Regularization (mathematics),Mathematics,Compressed sensing,Pointwise | Journal |
Volume | Issue | ISSN |
8 | 1 | 1936-4954 |
Citations | PageRank | References |
2 | 0.39 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hayden Schaeffer | 1 | 71 | 7.26 |
| Yi Yang | 2 | 92 | 9.96 |
| Stanley Osher | 3 | 7973 | 514.62 |