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}.
@article{arxiv.2407.17996,
title = {Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography},
author = {Kailai Zhou and Lijing Cai and Yibo Wang and Mengya Zhang and Bihan Wen and Qiu Shen and Xun Cao},
journal= {arXiv preprint arXiv:2407.17996},
year = {2024}
}