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Projection Onto Convex Sets (POCS) Based Signal Reconstruction Framework with an associated cost function
| Content Provider | Semantic Scholar |
|---|---|
| Author | Toghi, Mohammad Kose, Kivanc Çetin, A. Enis |
| Copyright Year | 2015 |
| Abstract | A new signal processing framework based on the projections onto convex sets (POCS) is developed for solving convex optimization problems. The dimension of the minimization problem is lifted by one and the convex sets corresponding to the epigraph of the cost function are dened. 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 iterative optimization approach starts with an arbitrary initial estimate in R N+1 and orthogonal projections are performed onto epigraph set in a sequential manner at each step of the optimization problem. The method provides globally optimal solutions in total-variation (TV), ltered variation (FV), ‘1, ‘1, and entropic cost functions. New denoising and compressive sensing algorithms using the TV cost function are developed. The new algorithms do not require any of the regularization parameter adjustment. Simulation examples are presented. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.researchgate.net/profile/Kivanc_Kose/publication/260127324_Signal_Reconstruction_Framework_Based_On_Projections_Onto_Epigraph_Set_Of_A_Convex_Cost_Function_(PESC)/links/54b985910cf24e50e93dc781.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
