$Structured l2−l1 experiment design regularization approach for near real time enhancement of low resolution fractional SAR imagery$

Structured l2−l1 experiment design regularization approach for near real time enhancement of low resolution fractional SAR imagery

The descriptive experiment design regularization (DEDR) paradigm is aggregated with the variational analysis approach that combines the l 2 image metric with the l 1 sparse image gradient map metric structures in the solution space. The proposed l 2 −l 1 structured total variation DEDR (STV-DEDR) fr...

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Bibliographische Detailangaben
Hauptverfasser:	Shkvarko, Yuriy V., Tuxpan, Jose, Yanez, Israel
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Descriptive experiment design regularization fractional synthetic aperture radar image enhancement Image reconstruction Image resolution Imaging Measurement Radar imaging remote sensing structured total variation Synthetic aperture radar
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Beschreibung
Zusammenfassung:	The descriptive experiment design regularization (DEDR) paradigm is aggregated with the variational analysis approach that combines the l 2 image metric with the l 1 sparse image gradient map metric structures in the solution space. The proposed l 2 −l 1 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.