Hugging Face's logo

Datasets:
ageppert
/
single_photon_challenge_full_preprocessed

Tasks:
Image-to-Image
Modalities:
Image
Formats:
imagefolder
Size:
1K - 10K
Tags:
single-photon
denoising
computational-imaging
diffusion
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
 Community
1
single_photon_challenge_full_preprocessed
File size: 3,077 Bytes
36a1cd3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
---
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/}
}
```