Dev

Atom Probe Tomography (APT) — Dev Tier

(5 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
flight_path_error -0.12 – 0.18 mm
voltage_calibration 0.9952 – 1.0072 -
detection_efficiency 0.576 – 0.636 -
tip_radius_error -1.2 – 1.8 nm

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 EquivAPT + gradient 0.766 31.76 0.94 0.86 ✓ Certified Adapted from equivariant vision transformer for atomic imaging, 2025
2 DiffusionAPT + gradient 0.716 29.31 0.906 0.8 ✓ Certified Inspired by Chung et al., ICLR 2023 (score-based MRI)
3 APT-Former + gradient 0.707 28.25 0.886 0.85 ✓ Certified Moody et al., Microsc. Microanal. 30(2):341, 2024
4 TrajectoryPINN + gradient 0.624 24.67 0.792 0.8 ✓ Certified De Geuser & Gault, Annu. Rev. Mater. Res. 52:1, 2022
5 LISTA-APT + gradient 0.590 22.91 0.728 0.85 ✓ Certified Gregor & LeCun, ICML 2010; adapted for APT 2020
6 ResNet-ArtefactCorr + gradient 0.543 20.87 0.64 0.89 ✓ Certified Wei et al., Ultramicroscopy 206:112817, 2019
7 Tikhonov-Trajectory + gradient 0.539 21.31 0.66 0.81 ✓ Certified Geiser et al., Microsc. Microanal. 13(6):437, 2007
8 PnP-BM3D (APT) + gradient 0.494 19.15 0.557 0.9 ✓ Certified Danielyan et al., IEEE TIP 21(9):3884, 2012
9 Bas-Protocol + gradient 0.453 18.29 0.514 0.82 ✓ Certified Bas et al., Appl. Surf. Sci. 87-88:298, 1995

Visible Data Fields

y H_ideal spec_ranges

Dataset

Format: HDF5
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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