Public
MINFLUX Nanoscopy — Public Tier
(5 scenes)Full-access development tier with all data visible.
What you get
Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.
How to use
Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.
What to submit
Reconstructed signals (x_hat) and corrected spec as HDF5.
Parameter Specifications
✓
True spec visible — use these exact values for Scenario III oracle reconstruction.
| Parameter | Spec Range | True Value | Unit |
|---|---|---|---|
| beam_center_error | -1.0 – 2.0 | 0.5 | nm |
| photon_count | 200.0 – 1100.0 | 650.0 | photons |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
13.27 dB
SSIM 0.3991
Scenario II (Mismatch)
11.88 dB
SSIM 0.2385
Scenario III (Oracle)
19.75 dB
SSIM 0.4579
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 17.02 | 0.3838 | 14.94 | 0.2440 | 20.04 | 0.5013 |
| scene_01 | 14.23 | 0.2969 | 12.92 | 0.2098 | 19.23 | 0.5480 |
| scene_02 | 8.52 | 0.3871 | 8.01 | 0.2308 | 20.11 | 0.3417 |
| scene_03 | 13.30 | 0.5284 | 11.66 | 0.2694 | 19.61 | 0.4408 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DECODE + gradient | 0.764 | 30.7 | 0.927 | 0.93 | ✓ Certified | Speiser et al., Nat. Methods 2021 |
| 2 | ANNA-PALM + gradient | 0.759 | 31.2 | 0.933 | 0.87 | ✓ Certified | Ouyang et al., Nat. Biotechnol. 2018 |
| 3 | SPARCOM + gradient | 0.704 | 27.13 | 0.861 | 0.94 | ✓ Certified | Solomon et al., SIAM J. Imaging Sci. 2019 |
| 4 | MLE Localization + gradient | 0.701 | 27.2 | 0.863 | 0.92 | ✓ Certified | Balzarotti et al., Science 2017 |
Visible Data Fields
y
H_ideal
spec_ranges
x_true
true_spec
Dataset
Format: HDF5
Scenes: 5
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%