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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shkvarko, Yuriy V. Tuxpan, Jose Yanez, Israel |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Telecommunications, Center for Advanced Research and Education of the National Polytechnic Institute, CINVESTAV-IPN, Guadalajara, Mexico (Shkvarko, Yuriy V.; Tuxpan, Jose; Yanez, Israel) |
| Abstract | The descriptive experiment design regularization (DEDR) paradigm is aggregated with the variational analysis approach that combines the l2 image metric with the l1 sparse image gradient map metric structures in the solution space. The proposed l2−l1 structured total variation DEDR (STV-DEDR) framework is particularly adapted for enhanced imaging with low resolution side looking airborne radar/fractional SAR sensors putting in a single optimization frame adaptive SAR image despeckling and resolution enhancement that exploits the structured desired image sparseness properties. The STV-DEDR method implemented in a contractive mapping iterative fashion outperforms the competing nonparametric adaptive radar imaging techniques both in resolution enhancement and computational complexity as verified in the simulations. |
| Starting Page | 274 |
| Ending Page | 277 |
| File Size | 427549 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467324717 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-31 |
| Publisher Place | Netherlands |
| Access Restriction | Subscribed |
| Rights Holder | EUROPEAN MICROWAVE ASSOC |
| Subject Keyword | Measurement Image resolution image enhancement structured total variation Imaging Radar imaging remote sensing Descriptive experiment design regularization Synthetic aperture radar Image reconstruction fractional synthetic aperture radar |
| Content Type | Text |
| Resource Type | Article |
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