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Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography | SpringerLink
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Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

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 https://github.com/CalayZhou/JDM-HDRNet.

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Acknowledgments

This research was supported by National Key Research and Development Program of China (2023YFF0713300), National Natural Science Foundation of China under Grant (62071216), Leading Technology of Jiangsu Basic Research Plan under Grant (BK20192003), and National Research Foundation Singapore Competitive Research Program (award number CRP29-2022-0003). We thank the device support from NJU-ZPTECH University-Enterprise Joint Laboratory for Computational Spectral Imaging.

Author information

Authors and Affiliations

  1. School of Electronic Sciene and Engineering, Nanjing University, Nanjing, China

    Kailai Zhou, Lijing Cai, Yibo Wang, Mengya Zhang, Qiu Shen & Xun Cao

  2. Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University, Nanjing, China

    Qiu Shen & Xun Cao

  3. Nanyang Technological University, Singapore, Singapore

    Bihan Wen

Authors
  1. Kailai Zhou
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  2. Lijing Cai
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  3. Yibo Wang
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  6. Qiu Shen
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  7. Xun Cao
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Corresponding author

Correspondence to Qiu Shen .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Zhou, K. et al. (2025). Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15071. Springer, Cham. https://doi.org/10.1007/978-3-031-72624-8_2

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  • DOI: https://doi.org/10.1007/978-3-031-72624-8_2

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