  The integration of miniaturized spectrometers into mobile devices offers new
avenues for image quality enhancement and facilitates novel downstream tasks.
However, the broader application of spectral sensors in mobile photography is
hindered by the inherent complexity of spectral images and the constraints of
spectral imaging capabilities. To overcome these challenges, we propose a joint
RGB-Spectral decomposition model guided enhancement framework, which consists
of two steps: joint decomposition and prior-guided enhancement. Firstly, we
leverage the complementarity between RGB and Low-resolution Multi-Spectral
Images (Lr-MSI) to predict shading, reflectance, and material semantic priors.
Subsequently, these priors are seamlessly integrated into the established
HDRNet to promote dynamic range enhancement, color mapping, and grid expert
learning, respectively. Additionally, we construct a high-quality Mobile-Spec
dataset to support our research, and our experiments validate the effectiveness
of Lr-MSI in the tone enhancement task. This work aims to establish a solid
foundation for advancing spectral vision in mobile photography. The code is
available at \url{https://github.com/CalayZhou/JDM-HDRNet}.
