Hidden
Lucky Imaging — Hidden Tier
(3 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 |
|---|---|---|
| fried_parameter_(r0) | 13.6 – 19.6 | cm |
| frame_selection_threshold | 4.4 – 28.4 | - |
| isoplanatic_angle | 4.3 – 7.3 | arcsec |
| registration_error | -0.07 – 0.23 | px |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | Shift-and-Add + gradient | 0.488 | 19.83 | 0.59 | 0.77 | ✓ Certified | Fried, JOSA 1966 |
| 2 | Drizzle + gradient | 0.480 | 19.61 | 0.58 | 0.76 | ✓ Certified | Fruchter & Hook, PASP 2002 |
| 3 | SpeckleNet + gradient | 0.462 | 18.68 | 0.534 | 0.81 | ✓ Certified | Xin et al., ApJ 2022 |
| 4 | BDI + gradient | 0.439 | 18.11 | 0.505 | 0.78 | ✓ Certified | Law et al., ApJ 2006 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%