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Structured l2#x2212;l1 experiment design regularization approach for near real time enhancement of low resolution fractional SAR imagery

机译:用于低分辨率分数SAR图像近实时增强的结构化2-1实验设计正则化方法

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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.
机译:描述性实验设计正则化(DEDR)范式通过变分分析方法聚合,该方法将l 2 图像度量与l 1 稀疏图像梯度图度量结构结合在一起空间。拟议中的l 2 −l 1 结构化总变化DEDR(STV-DEDR)框架特别适用于将低分辨率侧视机载雷达/分数SAR传感器置于其中的增强成像利用结构化所需图像稀疏特性的单个优化帧自适应SAR图像去斑点和分辨率增强。如在仿真中所验证的,以压缩映射迭代方式实现的STV-DEDR方法在分辨率增强和计算复杂性方面均优于竞争性非参数自适应雷达成像技术。

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