Dev
Atomic Force Microscopy (AFM) — 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 |
|---|---|---|
| tip_shape_convolution | -0.15 – 0.15 | - |
| piezo_nonlinearity | -1.2 – 1.8 | - |
| thermal_drift | -0.24 – 0.36 | nm/s |
| scanner_hysteresis | -2.4 – 3.6 | - |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SPM-Former + gradient | 0.721 | 29.15 | 0.903 | 0.84 | ✓ Certified | Chen et al., Nano Letters 24:3891, 2024 |
| 2 | DiffusionAFM + gradient | 0.671 | 26.17 | 0.837 | 0.87 | ✓ Certified | Score-based diffusion for SPM image restoration, 2024 |
| 3 | Self-Sup AFM + gradient | 0.583 | 22.85 | 0.725 | 0.82 | ✓ Certified | Self-supervised tip artifact deconvolution, 2023 |
| 4 | DeepAFM + gradient | 0.555 | 21.52 | 0.669 | 0.86 | ✓ Certified | Somnath et al., NPJ Comput. Mater. 2021 |
| 5 | Wiener Deconv + gradient | 0.545 | 21.23 | 0.656 | 0.85 | ✓ Certified | Klapetek et al., Meas. Sci. Technol. 2011 |
| 6 | PnP-ADMM + gradient | 0.483 | 19.04 | 0.552 | 0.86 | ✓ Certified | Venkatakrishnan et al., IEEE GlobalSIP 2013 |
| 7 | Plane Fit + gradient | 0.433 | 17.51 | 0.476 | 0.84 | ✓ Certified | Nečas & Klapetek, Open Physics 2012 |
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%