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README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+---
+
+# LRID-simplified Dataset
+
+## 📖 Overview
+This is a **simplified version** of the **LRID (Long-Range Indoor/outdoor Dataset)** dataset, designed for low-light image denoising research. It is part of the PMN_TPAMI project.  
+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_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).