Hidden
Atomic Force Microscopy (AFM) — Hidden Tier
(3 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 |
|---|---|---|
| tip_shape_convolution | -0.15 – 0.15 | - |
| piezo_nonlinearity | -0.7 – 2.3 | - |
| thermal_drift | -0.14 – 0.46 | nm/s |
| scanner_hysteresis | -1.4 – 4.6 | - |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SPM-Former + gradient | 0.689 | 27.51 | 0.87 | 0.83 | ✓ Certified | Chen et al., Nano Letters 24:3891, 2024 |
| 2 | DiffusionAFM + gradient | 0.638 | 25.42 | 0.815 | 0.79 | ✓ Certified | Score-based diffusion for SPM image restoration, 2024 |
| 3 | DeepAFM + gradient | 0.537 | 21.09 | 0.65 | 0.83 | ✓ Certified | Somnath et al., NPJ Comput. Mater. 2021 |
| 4 | Wiener Deconv + gradient | 0.497 | 19.84 | 0.591 | 0.81 | ✓ Certified | Klapetek et al., Meas. Sci. Technol. 2011 |
| 5 | Self-Sup AFM + gradient | 0.471 | 19.45 | 0.572 | 0.74 | ✓ Certified | Self-supervised tip artifact deconvolution, 2023 |
| 6 | PnP-ADMM + gradient | 0.463 | 18.83 | 0.541 | 0.79 | ✓ Certified | Venkatakrishnan et al., IEEE GlobalSIP 2013 |
| 7 | Plane Fit + gradient | 0.383 | 16.14 | 0.408 | 0.79 | ✓ Certified | Nečas & Klapetek, Open Physics 2012 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%