LRID Dataset

📖 Overview

This is the full version of the Low-light Raw Image Denoising (LRID) dataset, designed for low-light image denoising research. It is part of the PMN_TPAMI project (Learnability Enhancement for Low-light Raw Denoising: A Data Perspective, TPAMI 2024).

GitHub: https://github.com/megvii-research/PMN/tree/TPAMI

🗂️ Dataset Structure

PMN_TPAMI 
 └─LRID       # Full LRID Dataset (Raw Data)
   ├─bias       # Dark Frames
   ├─bias-hot   # Dark Frames in hot mode of the sensor
   │
   ├─indoor_x5  # Indoor Scenes
   │ ├─100      # Long-Exposure Raw Data (ISO-100)
   │ │ ├─000    # Scene Number (25 frames per scene)
   │ │ └─...
   │ │
   │ ├─6400     # Low-Light Noisy Data (ISO-6400)
   │ │ ├─1      # Digital Gain (Low-Light Ratio)
   │ │ │ ├─000  # Scene Number (10 frames per scene)
   │ │ │ └─...
   │ │ └─...
   │ │
   │ ├─ref      # Long-Exposure Raw Data (ISO-100)
   │ │ ├─000    # Scene Number (ISO-100 reference frame and its JPEG image after *camera ISP*)
   │ │ └─...    
   │ │
   │ ├─GT       # Visualization of Scenes and Our Fusion Process
   │ │
   │ ├─npy      # Binary Data
   │ │ ├─GT_flow                  # Optical Flows for Alignment (by HDR+)
   │ │ ├─GT_aligns                # ISO-100 Frames after Alignment
   │ │ └─GT_align_ours            # GT after Multi-Frame Fusion
   │ │
   │ └─metadata_indoor_x5_gt.pkl  # Metadata such as WB, CCM, etc.
   │
   ├─outdoor_x3 # Outdoor Scenes
   │ └─...      # (Structure similar to indoor_x5)
   │
   ├─indoor_x3  # Indoor Scenes with ND Filter
   │ └─...      # (Structure similar to indoor_x5)
   │
   ├─outdoor_x5 # [Abandon] Extremely Dark Outdoor Scenes with Ultra-Long Exposure
   │ └─...      # (Structure similar to indoor_x5)
   │
   └─resources  # (Noise calibration results such as dark shading)

🎯 Key Features

• Full version:
  • ✅ Original images used for dataset creation are included.
  • ✅ Intermediate results are included.
  • ✅ All noisy frames used for training are included.
  • ✅ Dark frames (bias, bias-hot) are included for calibration.
  • ✅ Reference data (ref/) provides ISO-100 long-exposure RAW and its camera-processed JPEG for each scene.

📊 Scene Categories

Subset	Description	Gain	Lighting Condition
indoor_x5	Indoor scenes	5×	Low-light
outdoor_x3	Outdoor scenes	3×	Low-light
indoor_x3	Indoor scenes with ND filter	3×	Controlled lighting
outdoor_x5 (abandon)	Outdoor scenes	5×	Low-light (a little misalignment)

🔍 Contents Description

• Noisy frames (e.g., indoor_x5/6400/1/000/): contain the first frame of each burst in RAW format.
• Ground truth (npy/GT_align_ours): multi-frame fusion results stored as NumPy binaries (aligned and denoised).
• Reference (ref/): for each scene, an ISO-100 long-exposure RAW and its corresponding JPEG (processed by the camera ISP) are provided as ideal references.
• Metadata (.pkl files): include white balance gains, color correction matrices (CCM), and other camera parameters essential for accurate raw data processing.
• Dark frames (bias/, bias-hot/): sensor bias frames under normal and hot modes, useful for noise calibration and dark current subtraction.
• Resources (resources/): calibration data such as dark shading patterns.

⚙️ Usage Notes

• Ground truth images are generated by aligning and fusing multiple frames, providing high-quality clean references.
• The ref/ folders contain the ISO-100 long-exposure captures, which serve as near-ideal clean references for evaluating denoising performance.
• Metadata files are crucial for correctly interpreting the raw sensor data; please refer to them when applying any ISP pipeline.

🔗 Access

Simplified LRID Dataset: The simplified version with all evaluation frames (1 frames per scene) has been uploaded to https://huggingface.co/datasets/hansen97/LRID_simplified

📝 Citation

If you use this dataset in your research, please cite:

@inproceedings{feng2022learnability,
    author = {Feng, Hansen and Wang, Lizhi and Wang, Yuzhi and Huang, Hua},
    title = {Learnability Enhancement for Low-Light Raw Denoising: Where Paired Real Data Meets Noise Modeling},
    booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
    year = {2022},
    pages = {1436–1444},
    numpages = {9},
    location = {Lisboa, Portugal},
    series = {MM '22}
}

@ARTICLE{feng2023learnability,
  author={Feng, Hansen and Wang, Lizhi and Wang, Yuzhi and Fan, Haoqiang and Huang, Hua},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Learnability Enhancement for Low-Light Raw Image Denoising: A Data Perspective}, 
  year={2024},
  volume={46},
  number={1},
  pages={370-387},
  doi={10.1109/TPAMI.2023.3301502}
}

📧 Contact

For questions or issues, please contact Hansen at hansen97@outlook.com.