Public
STEM-EDX Elemental Mapping — Public Tier
(3 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 |
|---|---|---|---|
| absorption_correction_error | -3.0 – 6.0 | 1.5 | - |
| detector_solid_angle | -0.15 – 0.15 | 0.0 | sr |
| peak_overlap_(spectral) | -0.6 – 1.2 | 0.3 | - |
| bremsstrahlung_background | -0.15 – 0.15 | 0.0 | - |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
19.81 dB
SSIM 0.4840
Scenario II (Mismatch)
17.32 dB
SSIM 0.1061
Scenario III (Oracle)
19.62 dB
SSIM 0.2167
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 19.40 | 0.4889 | 16.96 | 0.1087 | 19.66 | 0.2303 |
| scene_01 | 20.39 | 0.4977 | 17.60 | 0.1075 | 19.60 | 0.2162 |
| scene_02 | 20.11 | 0.4612 | 17.67 | 0.0963 | 20.09 | 0.2040 |
| scene_03 | 19.34 | 0.4883 | 17.06 | 0.1120 | 19.15 | 0.2163 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | PhysEDX + gradient | 0.842 | 36.7 | 0.977 | 0.92 | ✓ Certified | Chen et al., Microsc. Microanal. 2024 |
| 2 | DiffEDX + gradient | 0.838 | 36.92 | 0.978 | 0.89 | ✓ Certified | Gao et al., NeurIPS 2024 |
| 3 | SwinEDX + gradient | 0.830 | 35.56 | 0.971 | 0.93 | ✓ Certified | Wang et al., npj Comput. Mater. 2023 |
| 4 | TransEDX + gradient | 0.809 | 33.93 | 0.96 | 0.93 | ✓ Certified | Li et al., Ultramicroscopy 2022 |
| 5 | N2V-EDX + gradient | 0.775 | 31.45 | 0.936 | 0.93 | ✓ Certified | Krull et al., NeurIPS 2019 |
| 6 | DnCNN-EDX + gradient | 0.710 | 28.1 | 0.883 | 0.88 | ✓ Certified | Kovarik et al., npj Comput. Mater. 2016 |
| 7 | NMF-EDX + gradient | 0.645 | 24.76 | 0.794 | 0.9 | ✓ Certified | Nicoletti et al., Nature 2013 |
| 8 | TV-EDX + gradient | 0.592 | 22.82 | 0.724 | 0.87 | ✓ Certified | Saghi et al., Ultramicroscopy 2011 |
| 9 | MLS-EDX + gradient | 0.508 | 19.53 | 0.576 | 0.91 | ✓ Certified | Statham, J. Anal. At. Spectrom. 1995 |
Visible Data Fields
y
H_ideal
spec_ranges
x_true
true_spec
Dataset
Format: HDF5
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%