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--- |
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license: mit |
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tags: |
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- RAW |
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- RGB |
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- ISP |
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- NTIRE |
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- '2025' |
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- image |
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- processing |
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- low-level |
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- vision |
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- cameras |
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pretty_name: RAW Image Restoration Dataset |
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size_categories: |
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- 100M<n<1B |
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--- |
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# RAW Image Restoration Dataset |
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## [NTIRE 2025 RAW Image Restoration](https://codalab.lisn.upsaclay.fr/competitions/21647) |
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- Link to the challenge: https://codalab.lisn.upsaclay.fr/competitions/21647 |
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- Link to the workshop: https://www.cvlai.net/ntire/2025/ |
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This dataset includes images **different smartphones**: iPhoneX, SamsungS9, Samsung21, Google Pixel 7-9, Oppo vivo x90. 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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- Many more things :) |
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### How are the RAW images? |
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- All the RAW images in this dataset have been standarized to follow a Bayer Pattern **RGGB**, and already white-black level corrected. |
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- Each RAW image was split into several crops of size `512x512x4`(`1024x1024x3` for the corresponding RGBs). You see the filename `{raw_id}_{patch_number}.npy`. |
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- For each RAW image, you can find the associated metadata `{raw_id}.pkl`. |
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- RGB images are the corresponding captures from the phone i.e., the phone imaging pipeline (ISP) output. The images are saved as lossless PNG 8bits. |
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- Scenes include indoor/outdoor, day/night, different ISO levels, different shutter speed levels. |
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### How to use this? |
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- RAW images are saved using the following code: |
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``` |
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import numpy as np |
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max_val = 2**12 -1 |
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raw = (raw * max_val).astype(np.uint16) |
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np.save(os.path.join(SAVE_PATH, f"raw.npy"), raw_patch) |
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``` |
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We save the images as `uint16` to preserve as much as precision as possible, while maintaining the filesize small. |
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- Therefore, you can load the RAW images in your Dataset class, and feed them into the model as follows: |
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``` |
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import numpy as np |
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raw = np.load("iphone-x-part2/0_3.npy") |
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max_val = 2**12 -1 |
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raw = (raw / max_val).astype(np.float32) |
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``` |
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- The associated metadata can be loaded using: |
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``` |
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import pickle |
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with open("metadata.pkl", "rb") as f: |
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meta_loaded = pickle.load(f) |
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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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Contact: marcos.conde@uni-wuerzburg.de |
