Uncertainty-Aware Hyperspectral Image Reconstruction From RGB Measurements Using Unrolled Sparse Coding

Reconstructing hyperspectral images (HSIs) from RGB measurements is a fundamentally ill-posed problem that requires an effective HSI prior. Recent deep learning-based approaches address this by leveraging data-driven priors. However, their performance is constrained by the limited availability of large-scale datasets, which restricts their practical applicability in scenarios that demand high levels of trust. Furthermore, these models often fail silently when exposed to unseen spectral data or objects, offering no indication of potential reconstruction errors. To overcome these limitations, we propose a novel RGB-to-HSI reconstruction framework that exploits the sparse priors of both HSI and RGB features, by associating them through shared sparse codes. The shared sparse representation is obtained by unrolling extragradient-based ISTA with a trained neural network, interpreted as weights of unique spectral bases to capture the intrinsic spectral structure. Moreover, in the proposed framework, the sparse modeling error naturally emerges as an empirical uncertainty score, effectively signaling the presence of unseen spectra. This score can be visualized and utilized to guide selective model refinement. Experimental results confirm that the proposed method not only achieves strong performance for RGB-to-HSI reconstruction, but also demonstrates the utility of the uncertainty score for detecting novel spectral content and informing data selection during model updates.