Large-scale numerical simulations are capable of generating data up to terabytes or even petabytes. As a promising method of data reduction, super-resolution (SR) has been widely studied in the scientific visualization community. However, most of them are based on deep convolutional neural networks (CNNs) or generative adversarial networks (GANs) and the scale factor needs to be determined before constructing the network. As a result, a single training session only supports a fixed factor and has poor generalization ability. To address these problems, this paper proposes a Feature-Enhanced Implicit Neural Representation (FFEINR) for spatio-temporal super-resolution of flow field data. It can take full advantage of the implicit neural representation in terms of model structure and sampling resolution. The neural representation is based on a fully connected network with periodic activation functions, which enables us to obtain lightweight models. The learned continuous representation can decode the low-resolution flow field input data to arbitrary spatial and temporal resolutions, allowing for flexible upsampling. The training process of FFEINR is facilitated by introducing feature enhancements for the input layer, which complements the contextual information of the flow field.To demonstrate the effectiveness of the proposed method, a series of experiments are conducted on different datasets by setting different hyperparameters. The results show that FFEINR achieves significantly better results than the trilinear interpolation method.

翻译：大规模数值模拟能够产生高达太字节甚至拍字节的数据。作为一种有前景的数据缩减方法，超分辨率技术在科学可视化领域得到了广泛研究。然而，现有方法大多基于深度卷积神经网络或生成对抗网络，且需在构建网络前确定放大倍数。这导致单次训练仅支持固定放大倍数，泛化能力较差。针对这些问题，本文提出了一种面向流场数据时空超分辨率的特征增强隐式神经表示方法。该方法可充分利用隐式神经表示在模型结构和采样分辨率方面的优势。其神经表示采用基于周期激活函数的全连接网络，能够获得轻量化模型。学习到的连续表示可将低分辨率流场输入数据解码为任意时空分辨率，实现灵活的上采样。通过在输入层引入特征增强来补充流场上下文信息，促进了FFEINR的训练过程。为验证所提方法的有效性，我们通过设置不同超参数在多个数据集上开展了一系列实验。结果表明，FFEINR取得了显著优于三线性插值方法的效果。