TV Subgradient-Guided Multi-Source Fusion for Spectral Imaging in Dual-Camera CASSI Systems
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910157866270720 |
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| author | Zhao, Weiqiang Liu, Tianzhu Gui, Yuzhe Bian, Wei Gu, Yanfeng |
| author_facet | Zhao, Weiqiang Liu, Tianzhu Gui, Yuzhe Bian, Wei Gu, Yanfeng |
| contents | Balancing spectral, spatial, and temporal resolutions is a key challenge in spectral imaging. The Dual-Camera Coded Aperture Snapshot Spectral Imaging (DC-CASSI) system alleviates this trade-off but suffers from severely ill-posed reconstruction problems due to its high compression ratio. Existing methods are constrained by scene-specific tuning or excessive reliance on paired training data. To address these issues, we propose a Total Variation (TV) subgradient-guided multi-source fusion framework for DC-CASSI reconstruction, comprising three core components: (1) An end-to-end Single-Disperser CASSI (SD-CASSI) observation model based on the tensor-form Kronecker $δ$, which establishes a rigorous mathematical foundation for physical constraints while enabling efficient adjoint operator implementation; (2) An adaptive spatial reference generator that integrates SD-CASSI's physical model and RGB subspace constraint, generating the reference image as reliable spatial prior; (3) A TV subgradient-guided regularization term that encodes local structural directions from the reference image into spectral reconstruction, achieving high-quality fused results. The framework is validated on simulated datasets and real-world datasets. Experimental results demonstrate that it achieves state-of-the-art reconstruction performance and robust noise resilience. This work not only establishes an interpretable theoretical foundation for subgradient-guided fusion but also provides a practical fusion-based paradigm for high-fidelity spectral image reconstruction in DC-CASSI systems. Source code: https://github.com/bestwishes43/ADMM-TVDS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TV Subgradient-Guided Multi-Source Fusion for Spectral Imaging in Dual-Camera CASSI Systems Zhao, Weiqiang Liu, Tianzhu Gui, Yuzhe Bian, Wei Gu, Yanfeng Computer Vision and Pattern Recognition Optics Balancing spectral, spatial, and temporal resolutions is a key challenge in spectral imaging. The Dual-Camera Coded Aperture Snapshot Spectral Imaging (DC-CASSI) system alleviates this trade-off but suffers from severely ill-posed reconstruction problems due to its high compression ratio. Existing methods are constrained by scene-specific tuning or excessive reliance on paired training data. To address these issues, we propose a Total Variation (TV) subgradient-guided multi-source fusion framework for DC-CASSI reconstruction, comprising three core components: (1) An end-to-end Single-Disperser CASSI (SD-CASSI) observation model based on the tensor-form Kronecker $δ$, which establishes a rigorous mathematical foundation for physical constraints while enabling efficient adjoint operator implementation; (2) An adaptive spatial reference generator that integrates SD-CASSI's physical model and RGB subspace constraint, generating the reference image as reliable spatial prior; (3) A TV subgradient-guided regularization term that encodes local structural directions from the reference image into spectral reconstruction, achieving high-quality fused results. The framework is validated on simulated datasets and real-world datasets. Experimental results demonstrate that it achieves state-of-the-art reconstruction performance and robust noise resilience. This work not only establishes an interpretable theoretical foundation for subgradient-guided fusion but also provides a practical fusion-based paradigm for high-fidelity spectral image reconstruction in DC-CASSI systems. Source code: https://github.com/bestwishes43/ADMM-TVDS. |
| title | TV Subgradient-Guided Multi-Source Fusion for Spectral Imaging in Dual-Camera CASSI Systems |
| topic | Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2509.10897 |