Duplicate from marcosv/rawir

Co-authored-by: Marcos V. Conde <marcosv@users.noreply.huggingface.co>

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README.md

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+---
+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

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