用双分支网络提升海温图像分辨率，捕捉精细海洋结构。
Dual-Branch State-Displacement Network for Sea Surface Temperature Super-Resolution
- 分频处理：小波变换分离高低频成分，针对性增强细节。
- 性能领先：在多个公开数据集上超越现有最佳方法。
- 适合气候研究者：特别适用于高精度海洋热力结构分析。
海表温度（SST）是全球气候变化的关键指标，但卫星获取的SST图像常因空间分辨率低，难以捕捉如海洋锋面等细尺度热力结构。为此，本文提出一种双分支状态位移网络（DBSD-Net）用于SST超分辨率重建。该网络采用双分支架构：一个基于离散小波变换的小波频率分支，显式分离低频与高频成分以实现针对性处理；另一个基于冻结预训练VGG主干的VGGUNet分支，提取多尺度语义特征。在小波分支中，引入结构状态空间模块（SSSM）与门控结构优化单元（GSR），高效建模长程依赖并增强结构完整性；同时设计位移门模块（DGM），学习位移场对高频细节进行几何感知调制，缓解空间变化的退化问题。在多个公开SST数据集上的实验表明，DBSD-Net优于现有最先进方法。
原文摘要 · Abstract (English)
Sea surface temperature (SST) is a critical indicator of global climate change, yet satellite-derived SST imagery often suffers from coarse spatial resolution, limiting the ability to capture fine-scale thermal structures such as ocean fronts. To address this, we propose a Dual-Branch State-Displacement Network (DBSD-Net) for SST super-resolution. DBSD-Net adopts a dual-branch architecture: a wavelet frequency branch that explicitly separates low and high-frequency components via discrete wavelet transform for targeted processing, and a VGGUNet branch that extracts multi-scale semantic features from a frozen pre-trained VGG backbone. Within the wavelet branch, we introduce a Structural State Space Module (SSSM) with a Gated Structure Refinement (GSR) unit to efficiently capture long-range dependencies and enhance structural integrity, and a Displacement Gate Module (DGM) that learns a displacement field for geometry-aware modulation of high-frequency details, thereby mitigating spatially varying degradation. Experiments on multiple public SST datasets demonstrate that DBSD-Net outperforms existing state-of-the-art methods.
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