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

• URL: http://arxiv.org/abs/2507.18998v2
• Date: Thu, 07 Aug 2025 08:21:29 GMT
• Title: GPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution
• Authors: Yongsong Huang, Tomo Miyazaki, Shinichiro Omachi,
• Abstract summary: Infrared Image Super-Resolution is challenged by the low contrast and sparse textures of infrared data.<n>GPSMamba is a framework that synergizes architectural guidance with non-causal supervision.
• Score: 4.063682271487617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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