Public
Talbot-Lau X-ray Grating Interferometry — 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 |
|---|---|---|---|
| grating_alignment_(rotation) | -0.1 – 0.2 | 0.05 | deg |
| inter_grating_distance_error | -0.2 – 0.4 | 0.1 | - |
| phase_stepping_error | -1.0 – 2.0 | 0.5 | perstep |
| grating_defect_fraction | -0.6 – 1.2 | 0.3 | - |
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 | GratingFormer + gradient | 0.741 | 30.04 | 0.917 | 0.87 | ✓ Certified | Grating interferometry transformer, 2024 |
| 2 | DPC-Net + gradient | 0.716 | 27.95 | 0.88 | 0.92 | ✓ Certified | Differential phase contrast CNN, 2021 |
| 3 | PCA Retrieval + gradient | 0.560 | 21.34 | 0.661 | 0.91 | ✓ Certified | Zanette et al., PMB 2012 |
| 4 | Phase Stepping + gradient | 0.557 | 21.54 | 0.67 | 0.87 | ✓ Certified | Weitkamp et al., Opt. Express 2005 |
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%