Name
Abstract | ||
|---|---|---|
A new deconvolution algorithm based on making orthogonal projections onto the epigraph set of a convex cost function is presented. In this algorithm, the dimension of the minimization problem is lifted by one and sets corresponding to the cost function and observations are defined. If the utilized cost function is convex in RN, the corresponding epigraph set is also convex in RN+1. The deconvolution algorithm starts with an arbitrary initial estimate in RN+1. At each iteration cycle of the algorithm, first deconvolution projections are performed onto the hyperplanes representing observations, then an orthogonal projection is performed onto epigraph of the cost function. The method provides globally optimal solutions for total variation, l1, l2, and entropic cost functions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SIU.2014.6830560 | Signal Processing and Communications Applications Conference |
Keywords | DocType | Volume |
costing,deconvolution,entropy,iterative methods,minimisation,set theory,convex cost function,deconvolution algorithm,deconvolution projections,entropic cost function,epigraph set,hyperplanes,iteration cycle,l1 cost function,l2 cost function,minimization problem,orthogonal projections,total variation cost function,Epigraph of a cost function,deconvolution,projection onto convex sets,total variation | Journal | abs/1402.5818 |
ISSN | Citations | PageRank |
2165-0608 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Tofighi | 1 | 65 | 8.74 |
| Alican Bozkurt | 2 | 11 | 4.39 |
| Kivanç Köse | 3 | 75 | 9.55 |
| A. Enis Çetin | 4 | 871 | 118.56 |