From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917949891149824 |
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| author | Leng, Yihong Li, Jiaojiao Xu, Haitao Song, Rui |
| author_facet | Leng, Yihong Li, Jiaojiao Xu, Haitao Song, Rui |
| contents | Current hyperspectral image (HSI) reconstruction methods primarily rely on image-level approaches, which are time-consuming to form abundant high-quality HSIs through imagers. In contrast, spectrometers offer a more efficient alternative by capturing high-fidelity point spectra, enabling pixel-level HSI reconstruction that balances accuracy and label efficiency. To this end, we introduce a pixel-level spectral super-resolution (Pixel-SSR) paradigm that reconstructs HSI from RGB and point spectra. Despite its advantages, Pixel-SSR presents two key challenges: 1) generalizability to novel scenes lacking point spectra, and 2) effective information extraction to promote reconstruction accuracy. To address the first challenge, a Gamma-modeled strategy is investigated to synthesize point spectra based on their intrinsic properties, including nonnegativity, a skewed distribution, and a positive correlation. Furthermore, complementary three-branch prompts from RGB and point spectra are extracted with a Dynamic Prompt Mamba (DyPro-Mamba), which progressively directs the reconstruction with global spatial distributions, edge details, and spectral dependency. Comprehensive evaluations, including horizontal comparisons with leading methods and vertical assessments across unsupervised and image-level supervised paradigms, demonstrate that ours achieves competitive reconstruction accuracy with efficient label consumption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction Leng, Yihong Li, Jiaojiao Xu, Haitao Song, Rui Computer Vision and Pattern Recognition Current hyperspectral image (HSI) reconstruction methods primarily rely on image-level approaches, which are time-consuming to form abundant high-quality HSIs through imagers. In contrast, spectrometers offer a more efficient alternative by capturing high-fidelity point spectra, enabling pixel-level HSI reconstruction that balances accuracy and label efficiency. To this end, we introduce a pixel-level spectral super-resolution (Pixel-SSR) paradigm that reconstructs HSI from RGB and point spectra. Despite its advantages, Pixel-SSR presents two key challenges: 1) generalizability to novel scenes lacking point spectra, and 2) effective information extraction to promote reconstruction accuracy. To address the first challenge, a Gamma-modeled strategy is investigated to synthesize point spectra based on their intrinsic properties, including nonnegativity, a skewed distribution, and a positive correlation. Furthermore, complementary three-branch prompts from RGB and point spectra are extracted with a Dynamic Prompt Mamba (DyPro-Mamba), which progressively directs the reconstruction with global spatial distributions, edge details, and spectral dependency. Comprehensive evaluations, including horizontal comparisons with leading methods and vertical assessments across unsupervised and image-level supervised paradigms, demonstrate that ours achieves competitive reconstruction accuracy with efficient label consumption. |
| title | From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.06852 |