虚拟合成孔径雷达：基于深度学习的斑点噪声抑制算法的综合数据集（CS CV）---Elva

合成孔径雷达（SAR）图像包含了大量的信息，但由于图像中存在斑点噪声，实际应用情况有限。近年来，基于深度学习的方法在去噪和图像恢复领域取得了显著的进步。然而，由于缺乏适合训练基于深层神经网络系统的数据，进一步的研究受到了阻碍。在本文中，我们提出了一种生成合成数据的标准方法，用于训练散斑抑制算法，并演示了一个实际用例来推进该领域的研究。

原文题目：Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms

原文：Synthetic Aperture Radar (SAR) images contain a huge amount of information, however, the number of practical use-cases is limited due to the presence of speckle noise in them. In recent years, deep learning based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by the lack of availability of data suitable for training deep neural network based systems. With this paper, we propose a standard way of generating synthetic data for the training of speckle reduction algorithms and demonstrate a use-case to advance research in this domain.

原文作者：Shrey Dabhi, Kartavya Soni, Utkarsh Patel, Priyanka Sharma, Manojkumar Parmar

原文链接：https://arxiv.org/abs/2004.11021