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README.md

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+---
+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/}
+}
+```

completed_chunks.txt

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+challenges/reconstruction/test_0.zip
+challenges/reconstruction/test_1.zip
+challenges/reconstruction/test_2.zip
+challenges/reconstruction/test_3.zip
+challenges/reconstruction/test_4.zip
+challenges/reconstruction/train_0.zip
+challenges/reconstruction/train_1.zip
+challenges/reconstruction/train_10.zip
+challenges/reconstruction/train_11.zip
+challenges/reconstruction/train_12.zip
+challenges/reconstruction/train_13.zip
+challenges/reconstruction/train_14.zip
+challenges/reconstruction/train_15.zip
+challenges/reconstruction/train_16.zip
+challenges/reconstruction/train_17.zip
+challenges/reconstruction/train_18.zip
+challenges/reconstruction/train_19.zip
+challenges/reconstruction/train_2.zip
+challenges/reconstruction/train_20.zip
+challenges/reconstruction/train_21.zip
+challenges/reconstruction/train_22.zip
+challenges/reconstruction/train_23.zip
+challenges/reconstruction/train_24.zip
+challenges/reconstruction/train_25.zip
+challenges/reconstruction/train_26.zip
+challenges/reconstruction/train_27.zip
+challenges/reconstruction/train_28.zip
+challenges/reconstruction/train_29.zip
+challenges/reconstruction/train_3.zip
+challenges/reconstruction/train_30.zip
+challenges/reconstruction/train_31.zip
+challenges/reconstruction/train_32.zip
+challenges/reconstruction/train_33.zip
+challenges/reconstruction/train_34.zip
+challenges/reconstruction/train_35.zip
+challenges/reconstruction/train_36.zip
+challenges/reconstruction/train_37.zip
+challenges/reconstruction/train_38.zip
+challenges/reconstruction/train_39.zip
+challenges/reconstruction/train_4.zip
+challenges/reconstruction/train_40.zip
+challenges/reconstruction/train_41.zip
+challenges/reconstruction/train_42.zip
+challenges/reconstruction/train_43.zip
+challenges/reconstruction/train_44.zip
+challenges/reconstruction/train_45.zip
+challenges/reconstruction/train_46.zip
+challenges/reconstruction/train_47.zip
+challenges/reconstruction/train_48.zip
+challenges/reconstruction/train_49.zip
+challenges/reconstruction/train_5.zip
+challenges/reconstruction/train_6.zip
+challenges/reconstruction/train_7.zip
+challenges/reconstruction/train_8.zip
+challenges/reconstruction/train_9.zip

metadata.json

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+{
+  "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."
+}