Hidden
Holography — 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 |
|---|---|---|
| wavelength | -0.35 – 1.15 | nm |
| prop_distance | -3.5 – 11.5 | μm |
| tilt | -0.35 – 1.15 | mrad |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | ScorePhase + gradient | 0.662 | 25.67 | 0.823 | 0.88 | ✓ Certified | Wei et al., ECCV 2025 |
| 2 | AutoPhase++ + gradient | 0.646 | 25.88 | 0.829 | 0.78 | ✓ Certified | Rivenson et al., ECCV 2024 |
| 3 | HolographyViT + gradient | 0.644 | 25.86 | 0.828 | 0.77 | ✓ Certified | Wang et al., ICCV 2024 |
| 4 | DiffusionPhase + gradient | 0.620 | 24.52 | 0.787 | 0.8 | ✓ Certified | Song et al., NeurIPS 2024 |
| 5 | CyclePhase + gradient | 0.599 | 23.11 | 0.735 | 0.87 | ✓ Certified | Ge et al., IEEE Photonics 2023 |
| 6 | PhaseFormer + gradient | 0.596 | 22.97 | 0.73 | 0.87 | ✓ Certified | Tian et al., ICCV 2024 |
| 7 | PhaseResNet + gradient | 0.562 | 21.87 | 0.684 | 0.85 | ✓ Certified | Baoqing et al., Optica 2023 |
| 8 | PhaseNet + gradient | 0.515 | 20.96 | 0.644 | 0.74 | ✓ Certified | Rivenson et al., LSA 2018 |
| 9 | LRGS + gradient | 0.478 | 19.68 | 0.583 | 0.74 | ✓ Certified | Choi et al., 2023 |
| 10 | GS/HIO + gradient | 0.474 | 18.87 | 0.543 | 0.84 | ✓ Certified | Fienup, Appl. Opt. 1982 |
| 11 | prDeep + gradient | 0.459 | 18.23 | 0.511 | 0.86 | ✓ Certified | Metzler et al., ICML 2018 |
| 12 | Gerchberg-Saxton + gradient | 0.448 | 17.85 | 0.493 | 0.86 | ✓ Certified | Gerchberg & Saxton, Optik 1972 |
| 13 | Error Reduction + gradient | 0.404 | 16.59 | 0.43 | 0.83 | ✓ Certified | Fienup, J. Opt. Soc. Am. 1982 |
| 14 | deep-PR + gradient | 0.401 | 16.69 | 0.435 | 0.8 | ✓ Certified | Asif et al., ICCP 2017 |
Dataset
Scenes: 5
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%