用可学习的低秩优化方法，从单张彩图重建更真实的高光谱图像。
USCTNet: A deep unfolding nuclear-norm optimization solver for physically consistent HSI reconstruction
- 将重建问题建模为带核范数正则的物理逆问题，联合估计相机感光与光照参数。
- 提出自适应低秩子空间奇异值阈值法，避免传统SVD计算开销与不稳。
- 适合需要高保真光谱重建的遥感、医学成像等应用，尤其对数据质量敏感场景。
从单张RGB图像重建高光谱图像（HSI）是一个病态问题，当相机光谱响应（CSS）和场景光照条件不准确时，重建结果可能违背物理规律。本文将RGB到HSI的重建问题建模为基于核范数正则化、在可学习变换域中的物理驱动逆问题，并显式估计CSS与光照以构建每轮迭代中的前向算子，确保色彩一致性。为避免奇异值阈值（SVT）所需的全量奇异值分解（SVD）带来的高成本与数值不稳定性，提出一种数据自适应的低秩子空间SVT算子。在此基础上，开发了专用于HSI重建的深度展开求解器USCTNet，其结合参数估计模块与可学习的近端更新机制。在标准基准上的大量实验表明，该方法在重建精度上持续优于现有基于RGB的先进方法。代码已开源：https://github.com/psykheXX/USCTNet-Code-Implementation.git。
原文摘要 · Abstract (English)
Reconstructing hyperspectral images (HSIs) from a single RGB image is ill-posed and can become physically inconsistent when the camera spectral sensitivity (CSS) and scene illumination are misspecified. We formulate RGB-to-HSI reconstruction as a physics-grounded inverse problem regularized by a nuclear norm in a learnable transform domain, and we explicitly estimate CSS and illumination to define the forward operator embedded in each iteration, ensuring colorimetric consistency. To avoid the cost and instability of full singular-value decompositions (SVDs) required by singular-value thresholding (SVT), we introduce a data-adaptive low-rank subspace SVT operator. Building on these components, we develop USCTNet, a deep unfolding solver tailored to HSI that couples a parameter estimation module with learnable proximal updates. Extensive experiments on standard benchmarks show consistent improvements over state-of-the-art RGB-based methods in reconstruction accuracy. Code: https://github.com/psykheXX/USCTNet-Code-Implementation.git
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