将气象物理规律融入生成对抗网络，提升台风图像超分辨率质量
Physics Encoded Spatial and Temporal Generative Adversarial Network for Tropical Cyclone Image Super-resolution
- 用物理约束卷积建模气旋运动规律，分离物理动态与纹理特征
- 在Digital Typhoon数据集上4倍超分，结构保真度和视觉质量更优
- 适合气象图像处理、气候建模等需要物理一致性研究的领域
高分辨率卫星图像对追踪热带气旋（TC）的生成、增强和路径至关重要。然而，现有基于深度学习的超分辨率方法常将卫星图像序列当作普通视频处理，忽略了控制云运动的底层大气物理规律。为此，我们提出一种物理编码的空间-时间生成对抗网络（PESTGAN）用于台风图像超分辨率。具体地，设计了一个解耦生成器架构，引入PhyCell模块，通过约束卷积近似涡度方程，并将得到的近似物理动态作为隐式潜在表示，实现物理动态与视觉纹理的分离。此外，采用双判别器框架，引入时序判别器以确保运动一致性与空间真实感。在Digital Typhoon数据集上进行4×上采样的实验表明，PESTGAN在结构保真度和感知质量方面表现更佳。相比现有方法，在保持像素级精度竞争力的同时，显著提升了气象学上合理的云结构重建能力，具有更高的物理保真度。
原文摘要 · Abstract (English)
High-resolution satellite imagery is indispensable for tracking the genesis, intensification, and trajectory of tropical cyclones (TCs). However, existing deep learning-based super-resolution (SR) methods often treat satellite image sequences as generic videos, neglecting the underlying atmospheric physical laws governing cloud motion. To address this, we propose a Physics Encoded Spatial and Temporal Generative Adversarial Network (PESTGAN) for TC image super-resolution. Specifically, we design a disentangled generator architecture incorporating a PhyCell module, which approximates the vorticity equation via constrained convolutions and encodes the resulting approximate physical dynamics as implicit latent representations to separate physical dynamics from visual textures. Furthermore, a dual-discriminator framework is introduced, employing a temporal discriminator to enforce motion consistency alongside spatial realism. Experiments on the Digital Typhoon dataset for 4$\times$ upscaling demonstrate that PESTGAN establishes a better performance in structural fidelity and perceptual quality. While maintaining competitive pixel-wise accuracy compared to existing approaches, our method significantly excels in reconstructing meteorologically plausible cloud structures with superior physical fidelity.
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