Add dataset card and metadata

README.md

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+---
+task_categories:
+- image-to-image
+---
+
+# MobileSpectralCCDataset
+
+This dataset accompanies the paper [Leveraging Multispectral Sensors for Color Correction in Mobile Cameras](https://huggingface.co/papers/2512.08441), presented at CVPR 2026.
+
+[Project Page](https://lucacogo.github.io/Mobile-Spectral-CC/) | [GitHub Repository](https://github.com/LucaCogo/Mobile-Spectral-CC)
+
+## Overview
+
+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).
+
+### 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.
+
+## Dataset Structure
+
+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).
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```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}
+}
+```