| --- |
| license: cc-by-nc-4.0 |
| --- |
| |
| # 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](mailto:hansen97@outlook.com). |
