| --- |
| license: mit |
| tags: |
| - RAW |
| - RGB |
| - ISP |
| - NTIRE |
| - '2025' |
| - image |
| - processing |
| - low-level |
| - vision |
| - cameras |
| pretty_name: RAW Image Restoration Dataset |
| size_categories: |
| - 100M<n<1B |
| --- |
| |
| # RAW Image Restoration Dataset |
| ## [NTIRE 2025 RAW Image Restoration](https://codalab.lisn.upsaclay.fr/competitions/21647) |
|
|
| - Link to the challenge: https://codalab.lisn.upsaclay.fr/competitions/21647 |
| - Link to the workshop: https://www.cvlai.net/ntire/2025/ |
|
|
| 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: |
| - Reconstruct RAW images from the sRGB counterpart |
| - Learn an ISP to process the RAW images into the sRGB (emulating the phone ISP) |
| - Add noise to the RAW images and train a denoiser |
| - Many more things :) |
|
|
| ### How are the RAW images? |
|
|
| - All the RAW images in this dataset have been standarized to follow a Bayer Pattern **RGGB**, and already white-black level corrected. |
| - 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`. |
| - For each RAW image, you can find the associated metadata `{raw_id}.pkl`. |
| - 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. |
| - Scenes include indoor/outdoor, day/night, different ISO levels, different shutter speed levels. |
|
|
| ### How to use this? |
|
|
| - RAW images are saved using the following code: |
| ``` |
| import numpy as np |
| max_val = 2**12 -1 |
| raw = (raw * max_val).astype(np.uint16) |
| np.save(os.path.join(SAVE_PATH, f"raw.npy"), raw_patch) |
| ``` |
| We save the images as `uint16` to preserve as much as precision as possible, while maintaining the filesize small. |
|
|
| - Therefore, you can load the RAW images in your Dataset class, and feed them into the model as follows: |
| ``` |
| import numpy as np |
| raw = np.load("iphone-x-part2/0_3.npy") |
| max_val = 2**12 -1 |
| raw = (raw / max_val).astype(np.float32) |
| ``` |
|
|
| - The associated metadata can be loaded using: |
| ``` |
| import pickle |
| with open("metadata.pkl", "rb") as f: |
| meta_loaded = pickle.load(f) |
| |
| print (meta_loaded) |
| ``` |
|
|
| ### Citation |
|
|
| Toward Efficient Deep Blind Raw Image Restoration, ICIP 2024 |
|
|
| ``` |
| @inproceedings{conde2024toward, |
| title={Toward Efficient Deep Blind Raw Image Restoration}, |
| author={Conde, Marcos V and Vasluianu, Florin and Timofte, Radu}, |
| booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, |
| pages={1725--1731}, |
| year={2024}, |
| organization={IEEE} |
| } |
| ``` |
|
|
| Contact: marcos.conde@uni-wuerzburg.de |
