Hidden
MINFLUX Nanoscopy — 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 |
|---|---|---|
| beam_center_error | -0.7 – 2.3 | nm |
| photon_count | 290.0 – 1190.0 | photons |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | ANNA-PALM + gradient | 0.615 | 24.55 | 0.788 | 0.77 | ✓ Certified | Ouyang et al., Nat. Biotechnol. 2018 |
| 2 | DECODE + gradient | 0.605 | 23.97 | 0.767 | 0.79 | ✓ Certified | Speiser et al., Nat. Methods 2021 |
| 3 | SPARCOM + gradient | 0.598 | 23.86 | 0.764 | 0.77 | ✓ Certified | Solomon et al., SIAM J. Imaging Sci. 2019 |
| 4 | MLE Localization + gradient | 0.589 | 22.94 | 0.729 | 0.84 | ✓ Certified | Balzarotti et al., Science 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%