Name
Title | ||
|---|---|---|
Signal Reconstruction Framework Based On Projections Onto Epigraph Set Of A Convex Cost Function (PESC). |
Abstract | ||
|---|---|---|
A new signal processing framework based on making orthogonal Projections onto the Epigraph Set of a Convex cost function (PESC) is developed. In this way it is possible to solve convex optimization problems using the well-known Projections onto Convex Set (POCS) approach. In this algorithm, the dimension of the minimization problem is lifted by one and a convex set corresponding to the epigraph of the cost function is defined. If the cost function is a convex function in $R^N$, the corresponding epigraph set is also a convex set in R^{N+1}. The PESC method provides globally optimal solutions for total-variation (TV), filtered variation (FV), L_1, L_2, and entropic cost function based convex optimization problems. In this article, the PESC based denoising and compressive sensing algorithms are developed. Simulation examples are presented. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Convex conjugate,Discrete mathematics,Mathematical optimization,Effective domain,Convex set,Subderivative,Convex function,Epigraph,Proper convex function,Mathematics,Convex analysis |
DocType | Volume | Citations |
Journal | abs/1402.2088 | 6 |
PageRank | References | Authors |
0.45 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Tofighi | 1 | 65 | 8.74 |
| Kivanç Köse | 2 | 75 | 9.55 |
| A. Enis Çetin | 3 | 871 | 118.56 |