InWaveSR: Topography-Aware Super-Resolution Network for Internal Solitary Waves
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909794165587968 |
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| author | Wang, Xinjie Li, Zhongrui Han, Peng Yuan, Chunxin Xu, Jiexin Wei, Zhiqiang Nie, Jie |
| author_facet | Wang, Xinjie Li, Zhongrui Han, Peng Yuan, Chunxin Xu, Jiexin Wei, Zhiqiang Nie, Jie |
| contents | The effective utilization of observational data is frequently hindered by insufficient resolution. To address this problem, we present a new spatio-temporal super-resolution (STSR) model, called InWaveSR. It is built on a deep learning framework with physical restrictions and can efficiently generate high-resolution data from low-resolution input, especially for data featuring internal solitary waves (ISWs). To increase generality and interpretation, the model InWaveSR uses the primitive Navier-Stokes equations as the constraint, ensuring that the output results are physically consistent. In addition, the proposed model incorporates an HF-ResBlock component that combines the attention mechanism and the Fast Fourier Transform (FFT) method to improve the performance of the model in capturing high-frequency characteristics. Simultaneously, in order to enhance the adaptability of the model to complicated bottom topography, an edge sampling and numerical pre-processing method are carried out to optimize the training process. On evaluations using the in-situ observational ISW data, the proposed InWaveSR achieved a peak signal-to-noise ratio (PSNR) score of 36.2, higher than those of the traditional interpolation method and the previous neural network. This highlights its significant superiority over traditional methods, demonstrating its excellent performance and reliability in high-resolution ISW reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14243 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InWaveSR: Topography-Aware Super-Resolution Network for Internal Solitary Waves Wang, Xinjie Li, Zhongrui Han, Peng Yuan, Chunxin Xu, Jiexin Wei, Zhiqiang Nie, Jie Signal Processing The effective utilization of observational data is frequently hindered by insufficient resolution. To address this problem, we present a new spatio-temporal super-resolution (STSR) model, called InWaveSR. It is built on a deep learning framework with physical restrictions and can efficiently generate high-resolution data from low-resolution input, especially for data featuring internal solitary waves (ISWs). To increase generality and interpretation, the model InWaveSR uses the primitive Navier-Stokes equations as the constraint, ensuring that the output results are physically consistent. In addition, the proposed model incorporates an HF-ResBlock component that combines the attention mechanism and the Fast Fourier Transform (FFT) method to improve the performance of the model in capturing high-frequency characteristics. Simultaneously, in order to enhance the adaptability of the model to complicated bottom topography, an edge sampling and numerical pre-processing method are carried out to optimize the training process. On evaluations using the in-situ observational ISW data, the proposed InWaveSR achieved a peak signal-to-noise ratio (PSNR) score of 36.2, higher than those of the traditional interpolation method and the previous neural network. This highlights its significant superiority over traditional methods, demonstrating its excellent performance and reliability in high-resolution ISW reconstruction. |
| title | InWaveSR: Topography-Aware Super-Resolution Network for Internal Solitary Waves |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.14243 |