| Paper ID | SPTM-7.6 |
| Paper Title |
FAST AND ROBUST ADMM FOR BLIND SUPER-RESOLUTION |
| Authors |
Yifan Ran, Wei Dai, Imperial College London, United Kingdom |
| Session | SPTM-7: Estimation Theory and Methods 1 |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SMDSP-SAP] Sparsity-aware Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Though the blind super-resolution problem is nonconvex in nature, recent advance shows the feasibility of a convex formulation which gives the unique recovery guarantee. However, the convexification procedure is coupled with a huge computational cost and is therefore of great interests to investigate fast algorithms. To do so, we adapt an operator splitting approach ADMM and combine it with a novel preconditioning scheme. Numerical results show that the convergence rate is significantly improved by around two orders of magnitudes compared to the currently most adopted solver CVX. Also, by a Lasso type of formulation, the proposed solver is able to keep its high resolvability even under 0 dB SNR setting. |
