Name
Abstract | ||
|---|---|---|
We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and provide relations to sparsity problems. We then propose a new optimization method for these functionals which is based on dynamic programming and the alternating direction method of multipliers (ADMM). A series of experiments shows that the proposed method yields very satisfactory jump-sparse and sparse reconstructions, respectively. We highlight the capability of the method by comparing it with classical and recent approaches such as TV minimization (jump-sparse signals), orthogonal matching pursuit, iterative hard thresholding, and iteratively reweighted $\\ell ^{1}$ minimization (sparse signals). |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TSP.2014.2329263 | Signal Processing, IEEE Transactions |
Keywords | DocType | Volume |
dynamic programming,iterative methods,signal reconstruction,ADMM,alternating direction method of multipliers,dynamic programming,inverse Potts energy functionals,iterative hard thresholding,iteratively reweighted minimization,jump-sparse signal recovery,optimization method,orthogonal matching pursuit,sparse reconstructions,ADMM,deconvolution,denoising,incomplete data,inverse Potts functional,jump-sparsity,piecewise constant signal,segmentation,sparsity | Journal | 62 |
Issue | ISSN | Citations |
14 | 1053-587X | 25 |
PageRank | References | Authors |
0.83 | 28 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Storath | 1 | 138 | 12.69 |
| andreas weinmann | 2 | 138 | 12.81 |
| Laurent Demaret | 3 | 116 | 8.56 |