Dev
EBSD — 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 |
|---|---|---|
| pattern_center | -2.4 – 3.6 | pixels |
| sample_tilt | 69.4 – 70.9 | deg |
| detector_distance | -0.6 – 0.9 | mm |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | PhysEBSD + gradient | 0.766 | 32.05 | 0.943 | 0.84 | ✓ Certified | Chen et al., Acta Mater. 2024 |
| 2 | SwinEBSD + gradient | 0.745 | 29.91 | 0.915 | 0.9 | ✓ Certified | Li et al., npj Comput. Mater. 2023 |
| 3 | TransEBSD + gradient | 0.721 | 28.47 | 0.89 | 0.9 | ✓ Certified | Wang et al., Acta Mater. 2022 |
| 4 | DiffEBSD + gradient | 0.716 | 28.32 | 0.887 | 0.89 | ✓ Certified | Gao et al., NeurIPS 2024 |
| 5 | PointEBSD + gradient | 0.565 | 21.59 | 0.672 | 0.9 | ✓ Certified | Foden et al., Ultramicroscopy 2022 |
| 6 | DnCNN-EBSD + gradient | 0.555 | 21.32 | 0.66 | 0.89 | ✓ Certified | Kaufmann et al., npj Comput. Mater. 2020 |
| 7 | DI-EBSD + gradient | 0.545 | 21.18 | 0.654 | 0.86 | ✓ Certified | Chen et al., Ultramicroscopy 2015 |
| 8 | TV-EBSD + gradient | 0.493 | 19.37 | 0.568 | 0.86 | ✓ Certified | Wilkinson et al., Mater. Charact. 2006 |
| 9 | Hough-EBSD + gradient | 0.462 | 18.27 | 0.513 | 0.87 | ✓ Certified | Krieger Lassen, J. Microsc. 1994 |
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%