Dual-Branch State-Displacement Network for Sea Surface Temperature Super-Resolution

Abstract

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 multiscale semantic features from a frozen pretrained VGG backbone. Within the wavelet branch, we introduce a structural state space module with a gated structure refinement unit to efficiently capture long-range dependencies and enhance structural integrity, and a displacement gate module 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 and exhibits greater robustness at larger upscaling factors.