| --- |
| task_categories: |
| - image-to-image |
| --- |
| |
| # MobileSpectralCCDataset |
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| This dataset accompanies the paper [Leveraging Multispectral Sensors for Color Correction in Mobile Cameras](https://huggingface.co/papers/2512.08441), presented at CVPR 2026. |
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| [Project Page](https://lucacogo.github.io/Mobile-Spectral-CC/) | [GitHub Repository](https://github.com/LucaCogo/Mobile-Spectral-CC) |
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| ## Overview |
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| The **MobileSpectralCCDataset** is a physically grounded synthetic dataset designed to support end-to-end color correction research using auxiliary multispectral (MS) sensors. It was constructed by aggregating and repurposing hyperspectral reflectance data from two publicly available datasets: [KAUST](https://fuqiangx.github.io/publication/li2021multispectral/) and [BJTU-UVA](https://arxiv.org/abs/2412.14925). |
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| ### Key Features: |
| - **Sensor Simulation**: Includes simulated high-resolution RGB and low-resolution multispectral measurements across a wide range of illuminants and camera spectral sensitivities. |
| - **Ground Truth**: Color references are rendered under the standard D65 illuminant. |
| - **Geometric Inconsistency**: Includes a misaligned version of the data featuring spatial offsets and realistic warping transformations (derived from the Zurich dataset) to mimic real-world dual-sensor system alignments. |
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| ## Dataset Structure |
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| The dataset consists of pairs of high-resolution RGB images and auxiliary low-resolution MS images, along with their corresponding ground-truth color-corrected versions. For more details on the generation process, please refer to the [official GitHub repository](https://github.com/LucaCogo/Mobile-Spectral-CC). |
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| ## Citation |
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| If you use this dataset in your research, please cite: |
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| ```bibtex |
| @inproceedings{leveraging2026cogo, |
| title={Leveraging Multispectral Sensors for Color Correction in Mobile Cameras}, |
| author={Luca Cogo, Marco Buzzelli, Simone Bianco, Javier Vazquez-Corral, Raimondo Schettini}, |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year={2026} |
| } |
| ``` |
