---
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 **adaptive similarity-flow-sum
registration**:

1. **Unpack** the last 256 binary frames from each photoncube
2. **Partition** frames into non-overlapping registration blocks of size 8
3. **Register** each block to the reference (last block) using global
   scale+translation search over candidates `[0.9, 0.94, 0.98, 1.0, 1.02, 1.06, 1.1]` with
   phase cross-correlation (overlap threshold = 0.45)
4. **Refine** alignment with dense TVL1 optical flow
   (`use_dense_flow=True`, `attachment=15`,
   `tightness=0.3`, `num_warp=5`)
5. **Warp and accumulate** all frames per accepted block with per-pixel
   validity masking
6. **Invert SPC response** → linear RGB flux via `flux = -log(1 - p) / 0.5`
7. **sRGB tonemap** → standard gamma curve
8. **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_adaptive/
  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_adaptive",
    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_adaptive"
```

## Preprocessing parameters

```json
{
  "source": "Single Photon Challenge reconstruction dataset",
  "source_url": "https://singlephotonchallenge.com/download",
  "algorithm": "adaptive_similarity_flow_sum",
  "K": 256,
  "reg_block_size": 8,
  "scale_candidates": [
    0.9,
    0.94,
    0.98,
    1.0,
    1.02,
    1.06,
    1.1
  ],
  "overlap_threshold": 0.45,
  "max_global_mse": null,
  "use_dense_flow": true,
  "flow_attachment": 15,
  "flow_tightness": 0.3,
  "num_warp": 5,
  "invert_response": true,
  "invert_factor": 0.5,
  "tonemap": true,
  "split": "all",
  "notes": "Measurements are preprocessed from raw photoncubes using: adaptive block-wise scale+translation registration with optional dense optical-flow refinement, followed by 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/}
}
```