| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-to-image |
| tags: |
| - single-photon |
| - denoising |
| - computational-imaging |
| - diffusion |
| pretty_name: Single Photon Challenge - Full Preprocessed |
| --- |
| |
| # Single Photon Challenge — Full Preprocessed Dataset |
|
|
| Preprocessed measurement/target PNG pairs derived from the |
| [Single Photon Challenge](https://singlephotonchallenge.com/) reconstruction dataset. |
|
|
| ## Source |
|
|
| The raw dataset (~425GB training, ~42GB test) is hosted by the |
| [WISION Lab](https://wisionlab.com/) at UW-Madison. Photoncubes contain 1024 |
| binary frames from a simulated single-photon camera, paired with ground-truth |
| RGB reconstructions. |
|
|
| - **Challenge website:** <https://singlephotonchallenge.com/> |
| - **Download page:** <https://singlephotonchallenge.com/download> |
| - **VisionSIM toolkit:** <https://visionsim.readthedocs.io/> |
|
|
| ## Preprocessing pipeline |
|
|
| Each photoncube was preprocessed using the same approach as the |
| [challenge FAQ naive sum](https://singlephotonchallenge.com/faq): |
|
|
| 1. **Average** the last 16 binary frames → detection probability in [0, 1] |
| 2. **Invert SPC response** (`invert_response=True`, `factor=0.5`) |
| → linear RGB flux via `flux = -log(1 - p) / factor` |
| 3. **sRGB tonemap** (`tonemap=True`) → standard gamma curve |
| 4. **Save** as uint8 PNG |
|
|
| Measurements and targets are stored as 800×800 RGB PNGs. |
|
|
| ## Dataset statistics |
|
|
| | Split | Measurements | Targets | Paired | |
| |-------|-------------|---------|--------| |
| | train | 1850 | 1850 | yes | |
| | test | 185 | 0 | no (test set has no ground truth) | |
| | **total** | **2035** | **1850** | | |
|
|
| ## Directory structure |
|
|
| ``` |
| single_photon_challenge_full_preprocessed/ |
| metadata.json |
| train/ |
| <scene>/<frame>_measurement.png |
| <scene>/<frame>_target.png |
| test/ |
| <scene>/<frame>_measurement.png |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| # Download the full preprocessed dataset |
| root = snapshot_download( |
| repo_id="ageppert/single_photon_challenge_full_preprocessed", |
| repo_type="dataset", |
| ) |
| |
| # Or use with the diffusion training codebase: |
| # Set in config.py: |
| # PREPROCESSED_DATA_CONFIG["dataset_source"] = "hf" |
| # PREPROCESSED_DATA_CONFIG["dataset_hf_repo"] = "ageppert/single_photon_challenge_full_preprocessed" |
| ``` |
|
|
| ## Preprocessing parameters |
|
|
| ```json |
| { |
| "source": "Single Photon Challenge reconstruction dataset", |
| "source_url": "https://singlephotonchallenge.com/download", |
| "num_frames": 16, |
| "invert_response": true, |
| "invert_factor": 0.5, |
| "tonemap": true, |
| "split": "all", |
| "notes": "Measurements are preprocessed from raw photoncubes using: naive sum averaging, SPC response inversion, and sRGB tonemapping. Saved as uint8 PNGs. Targets are copied from original ground-truth PNGs." |
| } |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the Single Photon Challenge: |
|
|
| ``` |
| @misc{singlephotonchallenge, |
| title={The Single Photon Challenge}, |
| author={Jungerman, Sacha and Ingle, Atul and Nousias, Sotiris and Wei, Mian and White, Mel and Gupta, Mohit}, |
| year={2025}, |
| url={https://singlephotonchallenge.com/} |
| } |
| ``` |
|
|
