Dev
Compressed Ultrafast Photography (CUP) — Dev Tier
(5 scenes)Blind evaluation tier — no ground truth available.
What you get
Measurements (y), ideal forward operator (H), and spec ranges only.
How to use
Apply your pipeline from the Public tier. Use consistency as self-check.
What to submit
Reconstructed signals and corrected spec. Scored server-side.
Parameter Specifications
🔒
True spec hidden — estimate parameters from spec ranges below.
| Parameter | Spec Range | Unit |
|---|---|---|
| dmd_encoding_error | -0.48 – 0.72 | - |
| streak_sweep_calibration | -1.2 – 1.8 | - |
| temporal_spatial_coupling | -2.4 – 3.6 | - |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DAUHST-CUP + gradient | 0.806 | 35.44 | 0.97 | 0.82 | ✓ Certified | Cai et al., NeurIPS 2022 (CUP) |
| 2 | STFormer-CUP + gradient | 0.792 | 33.34 | 0.956 | 0.88 | ✓ Certified | Wang et al., CVPR 2022 (CUP) |
| 3 | DiffusionCUP + gradient | 0.779 | 33.19 | 0.954 | 0.83 | ✓ Certified | Qiao et al., Nat. Photonics 2020 (updated 2024) |
| 4 | PnP-FastDVDnet + gradient | 0.748 | 30.71 | 0.927 | 0.85 | ✓ Certified | Tassano et al., CVPR 2020 (CUP) |
| 5 | DeSCI-CUP + gradient | 0.705 | 28.33 | 0.888 | 0.83 | ✓ Certified | Liu et al., IEEE TPAMI 2018 (CUP adapt.) |
| 6 | E2E-CNN-CUP + gradient | 0.635 | 24.89 | 0.799 | 0.83 | ✓ Certified | Liang et al., CVPR 2019 |
| 7 | GAP-TV + gradient | 0.483 | 19.51 | 0.575 | 0.79 | ✓ Certified | Yuan, ICSIP 2016 |
| 8 | TV-CUP + gradient | 0.447 | 18.17 | 0.508 | 0.81 | ✓ Certified | Gao et al., Nature 2014 |
| 9 | TwIST-CUP + gradient | 0.425 | 17.43 | 0.472 | 0.81 | ✓ Certified | Bioucas-Dias & Figueiredo, IEEE TIP 2007 (CUP) |
Visible Data Fields
y
H_ideal
spec_ranges
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%