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Snapshot Compressive Hyperspectral Image Reconstruction via Complementary Priors
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AI
DOI：10.1109/TCI.2026.3664689.png)
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Abstract
En 中文
Coded aperture snapshot spectral imaging (CASSI) systems compressively project 3D hyperspectral data onto 2D measurements, offering high imaging speed and data efficiency. However, existing CASSI reconstruction algorithms still suffer from suboptimal reconstruction quality due to the ill-posed nature of hyperspectral compressive sensing reconstruction, which demands effective prior modeling. This paper proposes a novel reconstruction framework that integrates multiple complementary priors, jointly modeling spectral low-rankness, spatial nonlocal self-similarity, and deep image priors to comprehensively capture the intrinsic structure of hyperspectral images across spectral and spatial domains. By combining the strengths of model-based and data-driven priors, the proposed method achieves both strong generalization and expressive capacity. To tackle the optimization challenges posed by multiple regularization terms and parameters, an efficient ADMM-based solver is developed, which decomposes the problem into subproblems with closed-form solutions or those solvable via plug-and-play denoisers. In addition, an adaptive noise estimation mechanism is introduced to automatically tune the regularization parameters, eliminating the need for manual parameter adjustment. Extensive experiments demonstrate that the proposed method consistently outperforms state-of-the-art approaches in terms of reconstruction accuracy and robustness across multiple datasets.
Keywords:
Compressed sensing
coded aperture snapshot spectral imaging
plug-and-play
image reconstruction
image prior
Journal
I
IF:
4.8
Papers:
127
Citations:
0