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arXiv · 2507.18998

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

Yongsong Huang·Tomo Miyazaki·Xiaofeng Liu·Shinichiro Omachi
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Abstract

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.

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Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu, Shinichiro Omachi. 2025-07-25. GPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution. https://arxiv.org/abs/2507.18998

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