Hidden
Compressed Ultrafast Photography (CUP) — Hidden Tier
(5 scenes)Fully blind server-side evaluation — no data download.
What you get
No data downloadable. Algorithm runs server-side on hidden measurements.
How to use
Package algorithm as Docker container / Python script. Submit via link.
What to submit
Containerized algorithm accepting y + H, outputting x_hat + corrected spec.
Parameter Specifications
🔒
True spec hidden — blind evaluation, only ranges available.
| Parameter | Spec Range | Unit |
|---|---|---|
| dmd_encoding_error | -0.28 – 0.92 | - |
| streak_sweep_calibration | -0.7 – 2.3 | - |
| temporal_spatial_coupling | -1.4 – 4.6 | - |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DAUHST-CUP + gradient | 0.764 | 32.77 | 0.95 | 0.78 | ✓ Certified | Cai et al., NeurIPS 2022 (CUP) |
| 2 | DiffusionCUP + gradient | 0.754 | 31.64 | 0.939 | 0.81 | ✓ Certified | Qiao et al., Nat. Photonics 2020 (updated 2024) |
| 3 | STFormer-CUP + gradient | 0.742 | 30.6 | 0.926 | 0.83 | ✓ Certified | Wang et al., CVPR 2022 (CUP) |
| 4 | PnP-FastDVDnet + gradient | 0.690 | 27.79 | 0.876 | 0.81 | ✓ Certified | Tassano et al., CVPR 2020 (CUP) |
| 5 | DeSCI-CUP + gradient | 0.650 | 25.5 | 0.818 | 0.84 | ✓ Certified | Liu et al., IEEE TPAMI 2018 (CUP adapt.) |
| 6 | E2E-CNN-CUP + gradient | 0.526 | 20.55 | 0.625 | 0.85 | ✓ Certified | Liang et al., CVPR 2019 |
| 7 | TV-CUP + gradient | 0.422 | 17.34 | 0.467 | 0.81 | ✓ Certified | Gao et al., Nature 2014 |
| 8 | GAP-TV + gradient | 0.395 | 16.09 | 0.406 | 0.86 | ✓ Certified | Yuan, ICSIP 2016 |
| 9 | TwIST-CUP + gradient | 0.357 | 15.19 | 0.363 | 0.8 | ✓ Certified | Bioucas-Dias & Figueiredo, IEEE TIP 2007 (CUP) |
Dataset
Scenes: 5
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%