Abstract
Single-pixel imaging (SPI) offers a cost-effective route to hyperspectral acquisition but struggles to recover high-fidelity spatial and spectral details under extremely low sampling rates, a severely ill-posed inverse problem. While deep learning has shown potential, existing data-driven methods demand large-scale pretraining datasets that are often impractical in hyperspectral imaging. To overcome this limitation, we propose an end-to-end physics-informed framework that leverages untrained neural networks and RGB guidance for joint hyperspectral reconstruction and super-resolution without any external training data. The framework comprises three physically grounded stages: (1) a Regularized Least-Squares method with RGB-derived Grayscale Priors (LS-RGP) that initializes the solution by exploiting cross-modal structural correlations; (2) an Untrained Hyperspectral Recovery Network (UHRNet) that refines the reconstruction through measurement consistency and hybrid regularization; and (3) a Transformer-based Untrained Super-Resolution Network (USRNet) that upsamples the spatial resolution via cross-modal attention, transferring high-frequency details from the RGB guide. Extensive experiments on benchmark datasets demonstrate that our approach significantly surpasses state-of-the-art algorithms in both reconstruction accuracy and spectral fidelity. Moreover, a proof-of-concept experiment using a physical single-pixel imaging system validates the framework's practical applicability, successfully reconstructing a 144-band hyperspectral data cube at a mere 6.25% sampling rate. The proposed method thus provides a robust, data-efficient solution for computational hyperspectral imaging.
Abstract (translated)
单像素成像（SPI）为超光谱采集提供了一种经济高效的途径，但在极低采样率下难以恢复高保真空间和光谱细节，这是一个严重的病态逆问题。尽管深度学习已显示出潜力，但现有数据驱动方法需要大规模预训练数据集，这在超光谱成像中往往不切实际。为克服此限制，我们提出了一种端到端的物理信息框架，该框架利用未训练神经网络和RGB引导，无需任何外部训练数据即可实现联合超光谱重建与超分辨率。该框架包含三个基于物理的阶段：（1）基于RGB灰度先验的正则化最小二乘法（LS-RGP），通过利用跨模态结构相关性来初始化解；（2）未训练超光谱恢复网络（UHRNet），通过测量一致性和混合正则化来细化重建；（3）基于Transformer的未训练超分辨率网络（USRNet），通过跨模态注意力上采样空间分辨率，从RGB引导中转移高频细节。在基准数据集上的大量实验表明，我们的方法在重建精度和光谱保真度两方面均显著超越最先进算法。此外，使用物理单像素成像系统的概念验证实验验证了该框架的实际适用性，成功以仅6.25%的采样率重建了144波段超光谱数据立方体。因此，所提出的方法为计算超光谱成像提供了一种鲁棒、数据高效的解决方案。
URL
https://arxiv.org/abs/2604.03572
