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README.md
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Link to the challenge: https://codalab.lisn.upsaclay.fr/competitions/21648
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Link to the workshop: https://www.cvlai.net/ntire/2025/
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This dataset includes images from three smartphones
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- Reconstruct RAW images from the sRGB counterpart
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- Learn an ISP to process the RAW images into the sRGB (emulating the phone ISP)
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- Add noise to the RAW images and train a denoiser
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print (meta_loaded)
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```
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Contact: [email protected]
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Link to the challenge: https://codalab.lisn.upsaclay.fr/competitions/21648
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Link to the workshop: https://www.cvlai.net/ntire/2025/
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This dataset includes images from three **smartphones**: iPhoneX, SamsungS9, Samsung21. You can use it for many tasks, these are some:
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- Reconstruct RAW images from the sRGB counterpart
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- Learn an ISP to process the RAW images into the sRGB (emulating the phone ISP)
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- Add noise to the RAW images and train a denoiser
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print (meta_loaded)
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```
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### Citation
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Toward Efficient Deep Blind Raw Image Restoration, ICIP 2024
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```
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@inproceedings{conde2024toward,
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title={Toward Efficient Deep Blind Raw Image Restoration},
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author={Conde, Marcos V and Vasluianu, Florin and Timofte, Radu},
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booktitle={2024 IEEE International Conference on Image Processing (ICIP)},
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pages={1725--1731},
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year={2024},
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organization={IEEE}
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}
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```
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BSRAW: Improving blind raw image super-resolution, WACV 2024
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```
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@inproceedings{conde2024bsraw,
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title={Bsraw: Improving blind raw image super-resolution},
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author={Conde, Marcos V and Vasluianu, Florin and Timofte, Radu},
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booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
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pages={8500--8510},
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year={2024}
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}
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```
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Contact: [email protected]
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