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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). |