论文标题
虚拟SAR：用于基于深度学习的斑点降噪算法的合成数据集
Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms
论文作者
论文摘要
合成孔径雷达（SAR）图像包含大量信息，但是，由于其中存在斑点噪声，实际用例的数量受到限制。近年来，基于深度学习的技术在DeNoising和图像恢复的领域取得了重大改进。但是，由于缺乏适合培训基于神经网络的系统的数据的可用性，因此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式，用于训练斑点还原算法，并展示了一种用例来推进该领域的研究。
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.
