Public
Compressed Ultrafast Photography (CUP) — Public Tier
(5 scenes)Full-access development tier with all data visible.
What you get
Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.
How to use
Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.
What to submit
Reconstructed signals (x_hat) and corrected spec as HDF5.
Parameter Specifications
✓
True spec visible — use these exact values for Scenario III oracle reconstruction.
| Parameter | Spec Range | True Value | Unit |
|---|---|---|---|
| dmd_encoding_error | -0.4 – 0.8 | 0.2 | - |
| streak_sweep_calibration | -1.0 – 2.0 | 0.5 | - |
| temporal_spatial_coupling | -2.0 – 4.0 | 1.0 | - |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
7.84 dB
SSIM 0.3015
Scenario II (Mismatch)
7.84 dB
SSIM 0.1991
Scenario III (Oracle)
10.94 dB
SSIM 0.3682
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 7.64 | 0.2894 | 7.66 | 0.2019 | 10.79 | 0.3739 |
| scene_01 | 7.65 | 0.3040 | 7.65 | 0.1960 | 10.77 | 0.3632 |
| scene_02 | 8.07 | 0.3073 | 8.08 | 0.2002 | 11.17 | 0.3690 |
| scene_03 | 8.02 | 0.3052 | 7.95 | 0.1985 | 11.04 | 0.3666 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DiffusionCUP + gradient | 0.868 | 38.8 | 0.985 | 0.93 | ✓ Certified | Qiao et al., Nat. Photonics 2020 (updated 2024) |
| 2 | STFormer-CUP + gradient | 0.841 | 36.15 | 0.974 | 0.95 | ✓ Certified | Wang et al., CVPR 2022 (CUP) |
| 3 | DAUHST-CUP + gradient | 0.828 | 35.91 | 0.973 | 0.9 | ✓ Certified | Cai et al., NeurIPS 2022 (CUP) |
| 4 | PnP-FastDVDnet + gradient | 0.787 | 32.57 | 0.949 | 0.91 | ✓ Certified | Tassano et al., CVPR 2020 (CUP) |
| 5 | E2E-CNN-CUP + gradient | 0.767 | 31.72 | 0.94 | 0.87 | ✓ Certified | Liang et al., CVPR 2019 |
| 6 | DeSCI-CUP + gradient | 0.751 | 30.02 | 0.917 | 0.92 | ✓ Certified | Liu et al., IEEE TPAMI 2018 (CUP adapt.) |
| 7 | GAP-TV + gradient | 0.673 | 26.2 | 0.838 | 0.88 | ✓ Certified | Yuan, ICSIP 2016 |
| 8 | TwIST-CUP + gradient | 0.632 | 24.19 | 0.775 | 0.9 | ✓ Certified | Bioucas-Dias & Figueiredo, IEEE TIP 2007 (CUP) |
| 9 | TV-CUP + gradient | 0.577 | 22.26 | 0.701 | 0.87 | ✓ Certified | Gao et al., Nature 2014 |
Visible Data Fields
y
H_ideal
spec_ranges
x_true
true_spec
Dataset
Format: HDF5
Scenes: 5
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%