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Inverse problems with nonnegative and sparse solutions: algorithms and application to the phase retrieval problem
| Content Provider | Scilit |
|---|---|
| Author | Muoi, Pham Quy Hào, Dinh Nho Sahoo, Sujit Kumar Tang, Dongliang Cong, Nguyen Huu Dang, Cuong |
| Copyright Year | 2018 |
| Description | Journal: Inverse Problems In this paper, we study a gradient-type method and a semismooth Newton method for minimization problems in regularizing inverse problems with nonnegative and sparse solutions. We propose a special penalty functional forcing the minimizers of regularized minimization problems to be nonnegative and sparse, and then we apply the proposed algorithms in a practical the problem. The strong convergence of the gradient-type method and the local superlinear convergence of the semismooth Newton method are proven. Then, we use these algorithms for the phase retrieval problem and illustrate their efficiency in numerical examples, particularly in the practical problem of optical imaging through scattering media where all the noises from experiment are presented. |
| Related Links | http://iopscience.iop.org/article/10.1088/1361-6420/aab6c9/pdf |
| ISSN | 02665611 |
| e-ISSN | 13616420 |
| DOI | 10.1088/1361-6420/aab6c9 |
| Journal | Inverse Problems |
| Issue Number | 5 |
| Volume Number | 34 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2018-03-15 |
| Access Restriction | Open |
| Subject Keyword | Journal: Inverse Problems Applied Mathematics Phase Retrieval Problem Nonnegative and Sparse Problems with Nonnegative |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Theoretical Computer Science Signal Processing Mathematical Physics Computer Science Applications |
