Hidden

Atom Probe Tomography (APT) — Hidden Tier

(5 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
flight_path_error -0.07 – 0.23 mm
voltage_calibration 0.9972 – 1.0092 -
detection_efficiency 0.586 – 0.646 -
tip_radius_error -0.7 – 2.3 nm

Hidden Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 EquivAPT + gradient 0.734 30.87 0.929 0.77 ✓ Certified Adapted from equivariant vision transformer for atomic imaging, 2025
2 DiffusionAPT + gradient 0.684 26.95 0.857 0.86 ✓ Certified Inspired by Chung et al., ICLR 2023 (score-based MRI)
3 APT-Former + gradient 0.613 24.5 0.786 0.77 ✓ Certified Moody et al., Microsc. Microanal. 30(2):341, 2024
4 TrajectoryPINN + gradient 0.565 21.81 0.682 0.87 ✓ Certified De Geuser & Gault, Annu. Rev. Mater. Res. 52:1, 2022
5 Tikhonov-Trajectory + gradient 0.515 20.67 0.63 0.78 ✓ Certified Geiser et al., Microsc. Microanal. 13(6):437, 2007
6 LISTA-APT + gradient 0.513 20.46 0.621 0.8 ✓ Certified Gregor & LeCun, ICML 2010; adapted for APT 2020
7 ResNet-ArtefactCorr + gradient 0.495 19.37 0.568 0.87 ✓ Certified Wei et al., Ultramicroscopy 206:112817, 2019
8 Bas-Protocol + gradient 0.411 17.18 0.459 0.78 ✓ Certified Bas et al., Appl. Surf. Sci. 87-88:298, 1995
9 PnP-BM3D (APT) + gradient 0.387 16.56 0.428 0.75 ✓ Certified Danielyan et al., IEEE TIP 21(9):3884, 2012

Dataset

Scenes: 5

Scoring Formula

0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)

PSNR: 40% SSIM: 40% Consistency: 20%
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