GPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution

Infrared Image Super-Resolution (IRSR) is challenged by the low contrast and sparse textures of infrared data, requiring robust long-range modeling to maintain global coherence. While State-Space Models like Mamba offer proficiency in modeling long-range dependencies for this task, their inherent 1D causal scanning mechanism fragments the global context of 2D images, hindering fine-detail restoration. To address this, we propose Global Phase and Spectral Prompt-guided Mamba (GPSMamba), a framework that synergizes architectural guidance with non-causal supervision. First, our Adaptive Semantic-Frequency State Space Module (ASF-SSM) injects a fused semantic-frequency prompt directly into the Mamba block, integrating non-local context to guide reconstruction. Then, a novel Thermal-Spectral Attention and Phase Consistency Loss provides explicit, non-causal supervision to enforce global structural and spectral fidelity. By combining these two innovations, our work presents a systematic strategy to mitigate the limitations of causal modeling. Extensive experiments demonstrate that GPSMamba achieves state-of-the-art performance, validating our approach as a powerful new paradigm for infrared image restoration. Code is available at https://github.com/yongsongH/GPSMamba.

红外图像超分辨率（Infrared Image Super-Resolution, IRSR）任务面临的核心挑战在于红外数据对比度偏低且纹理稀疏，亟需具备鲁棒性的长程建模能力以维持全局图像连贯性。尽管以Mamba为代表的状态空间模型（State-Space Models, SSM）在该任务的长程依赖建模中表现出色，但其固有的一维因果扫描机制会割裂二维图像的全局上下文，进而阻碍精细细节的复原。为解决上述问题，本文提出全局相位与光谱提示引导的Mamba（Global Phase and Spectral Prompt-guided Mamba, GPSMamba）框架，该框架将架构引导与非因果监督有机融合。首先，本文提出的自适应语义-频率状态空间模块（Adaptive Semantic-Frequency State Space Module, ASF-SSM）将融合后的语义-频率提示直接注入Mamba块中，通过整合非局部上下文来指导图像重建。其次，本文设计了一种全新的热光谱注意力与相位一致性损失（Thermal-Spectral Attention and Phase Consistency Loss），可提供显式的非因果监督，以强化全局结构与光谱保真度。通过结合这两项创新设计，本文提出了一套系统性的策略，以缓解因果建模带来的局限性。大量实验结果表明，GPSMamba达到了当前最优的性能，验证了所提方法是红外图像复原领域极具潜力的全新范式。代码已开源至：https://github.com/yongsongH/GPSMamba