{"ID":2890351,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18998","arxiv_id":"2507.18998","title":"GPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution","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.","short_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 mec...","url_abs":"https://arxiv.org/abs/2507.18998","url_pdf":"https://arxiv.org/pdf/2507.18998v3","authors":"[\"Yongsong Huang\",\"Tomo Miyazaki\",\"Xiaofeng Liu\",\"Shinichiro Omachi\"]","published":"2025-07-25T06:56:16Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":611768,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2890351,"paper_url":"https://arxiv.org/abs/2507.18998","paper_title":"GPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution","repo_url":"https://github.com/yongsongH/GPSMamba","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}