README.md

---
license: cc-by-nc-4.0
---

# LRID-simplified Dataset

## 📖 Overview
This is a **simplified 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).  
Compared to the full LRID dataset, this version retains only the essential components for **validation purposes** while significantly reducing the size.

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

## 🗂️ Dataset Structure
```
PMN_TPAMI 
 └─LRID_simplified                  # Simplified dataset for training & evaluation
     ├─bias                           # Dark frames (sensor bias)
     ├─bias-hot                       # Dark frames in hot mode of the sensor
     │
     ├─indoor_x5                      # Indoor scenes
     │   │─100                          # Long-Exposure Raw Data (ISO-100)
     │   │   ├─000                      # Scene Number (only the first frame of the original 25 is kept)
     │   │   └─...
     │   │
     │   ├─6400                       # Low-light noisy data (ISO-6400)
     │   │   ├─1                        # Digital gain (low-light ratio)
     │   │   │   ├─000                  # Scene number (only the first frame of the original 10 is kept)
     │   │   │   └─...
     │   │   └─...
     │   │
     │   ├─npy                        # Binary data
     │   │   └─GT_align_ours            # Ground truth after multi-frame fusion
     │   │
     │   ├─ref                        # Long-exposure reference data (ISO-100)
     │   │   ├─000                      # Scene number
     │   │   │   ├── *.dng                # RAW reference frame (ISO-100)
     │   │   │   └── *.jpeg               # JPEG image after camera ISP
     │   │   └─...
     │   │
     │   └─metadata_indoor_x5_gt.pkl  # Metadata (white balance, 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                     # Outdoor scenes (abandon)
     │   └─...                          # (Structure similar to indoor_x5)
     │
     └─resources                      # Noise calibration results (e.g., dark shading)
```

## 🎯 Key Features
- **Simplified version**:  
  - ✅ Only the **first noisy frame** per scene/setting is kept (originally 10 frames per scene for training).  
  - ✅ All ground truth images are retained.  
  - ✅ 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.  
  - ❌ Original images used for dataset creation are excluded.  
  - ❌ Intermediate results are excluded.  
  - ❌ Most noisy frames used for training are excluded.

## 📊 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
- This dataset is intended for **validation or testing** rather than full training due to the reduced number of noisy frames.  
- Each scene originally contained 10 frames; only the first frame (index `000`) is preserved in this simplified version.  
- 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
**Full LRID Dataset**: The complete version with all training frames (10 frames per scene) is currently being uploaded and will be available soon.

URL: https://huggingface.co/datasets/hansen97/LRID

## 📝 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](mailto:hansen97@outlook.com).