STSRNet: Deep Joint Space–Time Super-Resolution for Vector Field Visualization
STSRNet: Deep Joint Space–Time Super-Resolution for Vector Field Visualization
复制标题
STSRNet：矢量场可视化的深度联合时空超分辨率
DOI：
10.1109/mcg.2021.3097555
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发表时间：
2021
影响因子：
1.8
通讯作者：
Jun Liu
中科院分区：
文献类型：
--
作者：
Yifei An;Han;Guihua Shan;Guan Li;Jun Liu
我们提出了一种基于联合时空超分辨率深度学习的时变向量场数据模型STSRNet。我们的方法旨在从相应的低分辨率关键帧中重建高时间分辨率和高空间分辨率矢量场序列。对于大规模模拟，只有空间分辨率降低的时间步长子集的数据才能存储用于事后分析。在本文中，我们利用深度学习模型以两阶段架构捕获矢量场数据的非线性复杂变化：第一阶段向前和向后变形一对低空间分辨率（LSR）关键帧以生成中间LSR帧，第二阶段执行空间超分辨率以输出高分辨率序列。我们的方法是可扩展的，可以处理不同的数据集。通过定量和定性评估，我们用几个数据集证明了我们框架的有效性。
We propose STSRNet, a joint space–time super-resolution deep learning based model for time-varying vector field data. Our method is designed to reconstruct high temporal resolution and high spatial resolution vector fields sequence from the corresponding low-resolution key frames. For large scale simulations, only data from a subset of time steps with reduced spatial resolution can be stored for post hoc analysis. In this article, we leverage a deep learning model to capture the nonlinear complex changes of vector field data with a two-stage architecture: the first stage deforms a pair of low spatial resolution (LSR) key frames forward and backward to generate the intermediate LSR frames, and the second stage performs spatial super-resolution to output the high-resolution sequence. Our method is scalable and can handle different datasets. We demonstrate the effectiveness of our framework with several datasets through quantitative and qualitative evaluations.