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Sparsity-driven despeckling method with low memory usage

机译:具有低内存使用量的稀疏驱动的机测方法

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Speckle noise which is inherent to Synthetic Aperture Radar (SAR) imaging makes it difficult to detect targets and recognize spatial patterns on earth. Thus, despeckling is critical and used as a preprocessing step for smoothing homogeneous regions while preserving features such as edges and point scatterers. In this study, a low-memory version of the previously proposed sparsity-driven despeckling (SDD) method is proposed. All steps of the method are parallelized using OpenMP on CPU and CUDA on GPU. Execution time and despeckling performance are shown using real-world SAR images.
机译:合成孔径雷达(SAR)成像固有的斑点噪声使得难以检测目标并识别地球上的空间模式。因此,机除是关键的并且用作用于平滑均匀区域的预处理步骤,同时保持诸如边缘和点散射体的特征。在这项研究中,提出了先前提出的稀疏驱动的检测(SDD)方法的低存储器版本。该方法的所有步骤都在GPU上使用CPU和CUDA上的OpenMP并行化。使用现实世界SAR图像显示执行时间和检测性能。

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