Hidden
STEM-EDX Elemental Mapping — Hidden Tier
(3 scenes)Fully blind server-side evaluation — no data download.
What you get
No data downloadable. Algorithm runs server-side on hidden measurements.
How to use
Package algorithm as Docker container / Python script. Submit via link.
What to submit
Containerized algorithm accepting y + H, outputting x_hat + corrected spec.
Parameter Specifications
🔒
True spec hidden — blind evaluation, only ranges available.
| Parameter | Spec Range | Unit |
|---|---|---|
| absorption_correction_error | -2.1 – 6.9 | - |
| detector_solid_angle | -0.15 – 0.15 | sr |
| peak_overlap_(spectral) | -0.42 – 1.38 | - |
| bremsstrahlung_background | -0.15 – 0.15 | - |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SwinEDX + gradient | 0.723 | 30.17 | 0.919 | 0.77 | ✓ Certified | Wang et al., npj Comput. Mater. 2023 |
| 2 | PhysEDX + gradient | 0.716 | 29.83 | 0.914 | 0.76 | ✓ Certified | Chen et al., Microsc. Microanal. 2024 |
| 3 | TransEDX + gradient | 0.709 | 28.74 | 0.895 | 0.82 | ✓ Certified | Li et al., Ultramicroscopy 2022 |
| 4 | DiffEDX + gradient | 0.665 | 26.09 | 0.835 | 0.85 | ✓ Certified | Gao et al., NeurIPS 2024 |
| 5 | DnCNN-EDX + gradient | 0.569 | 22.57 | 0.714 | 0.79 | ✓ Certified | Kovarik et al., npj Comput. Mater. 2016 |
| 6 | N2V-EDX + gradient | 0.499 | 19.63 | 0.581 | 0.85 | ✓ Certified | Krull et al., NeurIPS 2019 |
| 7 | NMF-EDX + gradient | 0.415 | 17.18 | 0.459 | 0.8 | ✓ Certified | Nicoletti et al., Nature 2013 |
| 8 | MLS-EDX + gradient | 0.389 | 16.08 | 0.405 | 0.83 | ✓ Certified | Statham, J. Anal. At. Spectrom. 1995 |
| 9 | TV-EDX + gradient | 0.317 | 13.98 | 0.309 | 0.77 | ✓ Certified | Saghi et al., Ultramicroscopy 2011 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%