Dev
EELS — 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 |
|---|---|---|
| energy_dispersion | -0.0024 – 0.0036 | eV/channel |
| zero_loss_shift | -0.36 – 0.54 | eV |
| aberration | -2.4 – 3.6 | % |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SwinEELS + gradient | 0.767 | 31.84 | 0.941 | 0.86 | ✓ Certified | Wang et al., npj Comput. Mater. 2023 |
| 2 | TransEELS + gradient | 0.752 | 31.47 | 0.937 | 0.81 | ✓ Certified | Li et al., Ultramicroscopy 2022 |
| 3 | PhysEELS + gradient | 0.739 | 30.78 | 0.928 | 0.8 | ✓ Certified | Chen et al., Microsc. Microanal. 2024 |
| 4 | DiffEELS + gradient | 0.739 | 30.29 | 0.921 | 0.84 | ✓ Certified | Gao et al., NeurIPS 2024 |
| 5 | N2V-EELS + gradient | 0.612 | 23.43 | 0.748 | 0.89 | ✓ Certified | Krull et al., NeurIPS 2019 |
| 6 | DnCNN-EELS + gradient | 0.517 | 20.17 | 0.607 | 0.86 | ✓ Certified | Kovarik et al., npj Comput. Mater. 2016 |
| 7 | PowerLaw-EELS + gradient | 0.485 | 19.02 | 0.551 | 0.87 | ✓ Certified | Egerton, EELS in the EM, Springer 2011 |
| 8 | MLS-EELS + gradient | 0.429 | 17.62 | 0.481 | 0.8 | ✓ Certified | Verbeeck & Van Aert, Ultramicroscopy 2004 |
| 9 | ICA-EELS + gradient | 0.412 | 16.92 | 0.446 | 0.82 | ✓ Certified | Bosman et al., Ultramicroscopy 2006 |
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%