GPSMamba：一种用于红外图像超分辨率的全局相位与频谱提示引导的Mamba
GPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution
AI总结：
针对红外图像超分辨率中Mamba因果扫描破坏全局上下文的问题，提出GPSMamba框架，通过ASF-SSM模块注入语义-频率提示并引入热-频谱注意力与相位一致性损失实现非因果监督，达到SOTA性能。
摘要：
红外图像超分辨率（IRSR）受红外数据低对比度和稀疏纹理的挑战，需要稳健的长距离建模以保持全局一致性。虽然像Mamba这样的状态空间模型在此任务中具备建模长距离依赖的能力，但其固有的1D因果扫描机制碎片化了2D图像的全局上下文，阻碍了精细细节的恢复。为了解决这一问题，我们提出了全局相位与频谱提示引导的Mamba（GPSMamba），该框架将架构引导与非因果监督相协同。首先，我们的自适应语义-频率状态空间模块（ASF-SSM）将融合的语义-频率提示直接注入Mamba块，整合非局部上下文以指导重建。然后，一种新颖的热-频谱注意力和相位一致性损失提供了显式的非因果监督，以强制实现全局结构和频谱保真度。通过结合这两项创新，我们的工作提出了一种系统性策略来缓解因果建模的局限性。大量实验表明，GPSMamba实现了最先进的性能，验证了我们的方法是红外图像恢复的强大新范式。代码可在 https://github.com/yongsongH/GPSMamba 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
