metadata

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

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: [email protected]