论文标题
MRSARP：SAR图像超分辨率的分层深层生成性
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution
论文作者
论文摘要
从训练中汲取的生成模型可以用深度学习方法作为先验，在反向不确定的反问题中，包括从稀疏测量集中进行成像。在本文中，我们提出了一种新型的层次深度生成模型MRSARP，用于SAR图像，可以共同在不同的分辨率下合成目标的SAR图像。 MRSARP与一位评论家一起训练，该评论家共同评分多分辨率图像，以决定它们是否是不同分辨率下目标的现实图像。我们展示了如何使用这种深层生成模型从同一目标的低分辨率图像中检索高空间分辨率图像。修改了发电机的成本函数，以提高其为给定的一组分辨率图像检索输入参数的能力。我们使用用于评估模拟数据上超分辨率性能的三个标准误差指标评估模型的性能，并将其与基于稀疏性的图像锐化方法进行比较。
Generative models learned from training using deep learning methods can be used as priors in inverse under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel hierarchical deep-generative model MrSARP for SAR imagery that can synthesize SAR images of a target at different resolutions jointly. MrSARP is trained in conjunction with a critic that scores multi resolution images jointly to decide if they are realistic images of a target at different resolutions. We show how this deep generative model can be used to retrieve the high spatial resolution image from low resolution images of the same target. The cost function of the generator is modified to improve its capability to retrieve the input parameters for a given set of resolution images. We evaluate the model's performance using the three standard error metrics used for evaluating super-resolution performance on simulated data and compare it to upsampling and sparsity based image sharpening approaches.
