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 | ||
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. |