HADAR-Based Thermal Infrared Hyperspectral Image Restoration
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913123653386240 |
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| author | Dai, Cheng Lin, Jiale Song, Bingxuan Chen, Yifei Chen, Jiashuo Yuan, Xin Bao, Fanglin |
| author_facet | Dai, Cheng Lin, Jiale Song, Bingxuan Chen, Yifei Chen, Jiashuo Yuan, Xin Bao, Fanglin |
| contents | Thermal-infrared (TIR) hyperspectral imagery (HSI) provides critical scene information for various applications. However, its practical utility is severely limited by unique sensor degradations beyond the capabilities of existing restoration methods, which are ignorant of underlying thermal physics. Here, we propose HAIR (HADAR-based Image Restoration) as a physics-driven framework for ground-based TIR-HSI restoration. HAIR utilizes the HADAR rendering equation (HRE) and combines it with the atmospheric downwelling radiative transfer equation (RTE) to model TIR-HSI using temperature, emissivity, and texture (TeX) physical triplets. This physical model leads to a TeX decompose-synthesize strategy that guarantees physical consistency and spatio-spectral noise resilience, in stark contrast to existing approaches. Moreover, our framework uses a forward-modeled atmospheric downwelling reference, along with spectral smoothness of emissivity and blackbody radiation, to enable spectral calibration and generation that would otherwise be elusive. Our extensive experiments on the outdoor DARPA Invisible Headlights dataset and in-lab FTIR measurements show that HAIR consistently outperforms state-of-the-art methods across denoising, inpainting, spectral calibration, and spectral super-resolution, establishing a benchmark in objective accuracy and visual quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13664 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HADAR-Based Thermal Infrared Hyperspectral Image Restoration Dai, Cheng Lin, Jiale Song, Bingxuan Chen, Yifei Chen, Jiashuo Yuan, Xin Bao, Fanglin Computer Vision and Pattern Recognition Optics Thermal-infrared (TIR) hyperspectral imagery (HSI) provides critical scene information for various applications. However, its practical utility is severely limited by unique sensor degradations beyond the capabilities of existing restoration methods, which are ignorant of underlying thermal physics. Here, we propose HAIR (HADAR-based Image Restoration) as a physics-driven framework for ground-based TIR-HSI restoration. HAIR utilizes the HADAR rendering equation (HRE) and combines it with the atmospheric downwelling radiative transfer equation (RTE) to model TIR-HSI using temperature, emissivity, and texture (TeX) physical triplets. This physical model leads to a TeX decompose-synthesize strategy that guarantees physical consistency and spatio-spectral noise resilience, in stark contrast to existing approaches. Moreover, our framework uses a forward-modeled atmospheric downwelling reference, along with spectral smoothness of emissivity and blackbody radiation, to enable spectral calibration and generation that would otherwise be elusive. Our extensive experiments on the outdoor DARPA Invisible Headlights dataset and in-lab FTIR measurements show that HAIR consistently outperforms state-of-the-art methods across denoising, inpainting, spectral calibration, and spectral super-resolution, establishing a benchmark in objective accuracy and visual quality. |
| title | HADAR-Based Thermal Infrared Hyperspectral Image Restoration |
| topic | Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2605.13664 |