Dev
STEM-EDX Elemental Mapping — Dev Tier
(3 scenes)Blind evaluation tier — no ground truth available.
What you get
Measurements (y), ideal forward operator (H), and spec ranges only.
How to use
Apply your pipeline from the Public tier. Use consistency as self-check.
What to submit
Reconstructed signals and corrected spec. Scored server-side.
Parameter Specifications
🔒
True spec hidden — estimate parameters from spec ranges below.
| Parameter | Spec Range | Unit |
|---|---|---|
| absorption_correction_error | -3.6 – 5.4 | - |
| detector_solid_angle | -0.15 – 0.15 | sr |
| peak_overlap_(spectral) | -0.72 – 1.08 | - |
| bremsstrahlung_background | -0.15 – 0.15 | - |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SwinEDX + gradient | 0.782 | 33.09 | 0.953 | 0.85 | ✓ Certified | Wang et al., npj Comput. Mater. 2023 |
| 2 | PhysEDX + gradient | 0.758 | 30.85 | 0.929 | 0.89 | ✓ Certified | Chen et al., Microsc. Microanal. 2024 |
| 3 | DiffEDX + gradient | 0.749 | 30.79 | 0.928 | 0.85 | ✓ Certified | Gao et al., NeurIPS 2024 |
| 4 | TransEDX + gradient | 0.742 | 30.58 | 0.925 | 0.83 | ✓ Certified | Li et al., Ultramicroscopy 2022 |
| 5 | DnCNN-EDX + gradient | 0.637 | 24.84 | 0.797 | 0.85 | ✓ Certified | Kovarik et al., npj Comput. Mater. 2016 |
| 6 | N2V-EDX + gradient | 0.621 | 24.33 | 0.78 | 0.83 | ✓ Certified | Krull et al., NeurIPS 2019 |
| 7 | NMF-EDX + gradient | 0.475 | 18.65 | 0.532 | 0.88 | ✓ Certified | Nicoletti et al., Nature 2013 |
| 8 | MLS-EDX + gradient | 0.446 | 18.32 | 0.516 | 0.78 | ✓ Certified | Statham, J. Anal. At. Spectrom. 1995 |
| 9 | TV-EDX + gradient | 0.420 | 16.99 | 0.45 | 0.85 | ✓ Certified | Saghi et al., Ultramicroscopy 2011 |
Visible Data Fields
y
H_ideal
spec_ranges
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%