Public
Optical Diffraction Tomography (ODT) — 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 |
|---|---|---|---|
| illumination_angle_error | -0.4 – 0.8 | 0.2 | degperangle |
| missing_cone_artifact | 26.0 – 38.0 | 32.0 | deg |
| refractive_index_of_medium | 1.3344 – 1.3422 | 1.3383 | - |
| multiple_scattering | -2.0 – 4.0 | 1.0 | - |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
22.61 dB
SSIM 0.7075
Scenario II (Mismatch)
20.21 dB
SSIM 0.1585
Scenario III (Oracle)
23.95 dB
SSIM 0.1525
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 23.97 | 0.7060 | 21.29 | 0.1538 | 24.36 | 0.1416 |
| scene_01 | 22.69 | 0.6936 | 20.16 | 0.1525 | 23.95 | 0.1560 |
| scene_02 | 21.95 | 0.7536 | 20.50 | 0.1715 | 23.95 | 0.1510 |
| scene_03 | 21.82 | 0.6767 | 18.89 | 0.1561 | 23.54 | 0.1613 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | Rytov-Former + gradient | 0.766 | 31.28 | 0.934 | 0.9 | ✓ Certified | ODT reconstruction transformer, 2024 |
| 2 | ODT-Net + gradient | 0.742 | 30.07 | 0.918 | 0.87 | ✓ Certified | Zhou et al., Light: S&A 2023 |
| 3 | Born-ADMM + gradient | 0.661 | 25.55 | 0.819 | 0.89 | ✓ Certified | Lim et al., Phys. Rev. Lett. 2015 |
| 4 | Wolf FBP + gradient | 0.570 | 21.77 | 0.68 | 0.9 | ✓ Certified | Wolf, Opt. Commun. 1969 |
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%