Create README.md

README.md

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+---
+license: mit
+tags:
+- RAW
+- RGB
+- ISP
+- NTIRE
+- '2025'
+- image
+- processing
+- low-level
+- vision
+- cameras
+pretty_name: NTIRE 2025 RGB2RAW Dataset
+size_categories:
+- 100M<n<1B
+---
+
+# RGB2RAW Dataset
+## [NTIRE 2025 RAW Image Reconstruction from sRGB](https://codalab.lisn.upsaclay.fr/competitions/21648)
+
+Link to the challenge: https://codalab.lisn.upsaclay.fr/competitions/21648
+Link to the workshop: https://www.cvlai.net/ntire/2025/
+
+This dataset includes images from three smartphones: iPhoneX, SamsungS9, Samsung21. 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 :)
+
+### Files
+
+- `val_rgbs_in.zip` includes the validation RGB images for the NTIRE 2025 challenge. You need to unprocess them, obtain the RAW images, save the RAW images (see the code below), and submit them. The website for the submission is: https://codalab.lisn.upsaclay.fr/competitions/21648
+- `train_rgb2raw.zip` includes the training data, many many raw-rgb pairs {.npy} and {.png}.
+
+### 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)
+  ```
+
+
+Contact: [email protected]