Hidden
PALM/STORM — 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 |
|---|---|---|
| psf_model | -3.5 – 11.5 | GaussianvsAiry |
| emitter_density | -14.0 – 46.0 | % |
| drift | -0.35 – 1.15 | nm/frame |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DECODE + gradient | 0.608 | 23.55 | 0.752 | 0.86 | ✓ Certified | Speiser et al., Nat. Methods 2021 |
| 2 | Deep-STORM + gradient | 0.478 | 18.86 | 0.543 | 0.86 | ✓ Certified | Nehme et al., Optica 2018 |
| 3 | FALCON + gradient | 0.474 | 19.21 | 0.56 | 0.79 | ✓ Certified | Min et al., Sci. Rep. 2014 |
| 4 | ThunderSTORM + gradient | 0.408 | 17.19 | 0.46 | 0.76 | ✓ Certified | Ovesny et al., Bioinformatics 2014 |
Dataset
Scenes: 5
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%