发表机构
Baidu, Inc.（百度公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有气候数据超分辨率模型忽略时间相关性的问题，提出带双向时间对齐的Temporal-Enhanced框架，通过配对潜在映射、双向时间对齐和时序增强超分实现性能提升，在大规模真实数据集上表现优越。
AI 中文摘要
高分辨率气候数据对气象预测及各领域决策支持至关重要，但获取此类数据成本极高，因此需开发数据驱动的气象预测模型，这类模型旨在从低分辨率输入生成细粒度气候数据，该过程称为气候数据超分辨率（SR）。然而，近期深度学习在气候数据SR领域的进展主要聚焦于利用单帧空间信息，很大程度上忽略了可提升SR结果的不同时间帧间的时间相关性。此外，气候数据具有内在随机性和噪声，使得光流模型等广泛使用的时间对齐方法在此场景下失效。因此，开发一种适配气候数据SR、能有效捕捉隐含时间相关性的框架仍是未解决的挑战。为此，我们提出一种带有双向时间对齐的新型Temporal-Enhanced框架。本质上，该框架通过双向对齐建立时间桥，以提升气候数据SR中的空间分辨率，进而改善SR性能。在该框架内，Paired Latent Mapping通过统一潜在空间实现空间对齐与降噪；随后，Bidirectional Temporal Alignment通过在连续潜在帧上训练正向与反向网络来捕捉时间相关性；最后，Temporal Enhanced Super-resolution对整个框架进行优化以用于气候数据SR。在大规模真实数据集上的实验证明了我们框架的优越性能。
英文摘要
High-resolution climate data is crucial for meteorological predictions and for informing decision support across diverse domains. However, the acquisition of such high-resolution climate information is often prohibitively costly, necessitating the development of data-driven meteorological prediction models. These models aim to generate fine-grained climate data from low-resolution inputs, a process termed climate data super-resolution (SR). Nevertheless, recent advancements in deep learning for climate data SR have primarily focused on leveraging single-frame spatial information, largely neglecting the temporal correlations between different time frames that could enhance SR outcomes. Furthermore, climate data are inherently stochastic and noisy, rendering widely used temporal alignment methods, such as optical flow models, ineffective in this context. Consequently, the development of a framework tailored for climate data SR that effectively captures implicit temporal correlations remains an unresolved challenge. To this end, we propose a novel Temporal-Enhanced framework with bidirectional temporal alignment. In essence, our framework establishes a temporal bridge to enhance spatial resolution in climate data SR through bidirectional alignment, leading to improved SR performance. Within this framework, Paired Latent Mapping achieves spatial alignment and noise reduction by unifying latent spaces. Then a Bidirectional Temporal Alignment captures temporal correlations by training forward and backward networks on consecutive latent frames. Temporal Enhanced Super-resolution then optimizes the entire framework for climate data SR. Experiments on large-scale real-world datasets demonstrated the superior performance of our framework.
