Dev
Near-field Scanning Optical Microscopy (NSOM) — 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_sample_distance | 0.4 – 24.4 | nm |
| aperture_size_error | -4.8 – 7.2 | - |
| topographic_coupling | -7.2 – 10.8 | - |
| far_field_background | -4.8 – 7.2 | - |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DiffusionSPM + gradient | 0.677 | 26.66 | 0.85 | 0.85 | ✓ Certified | Zhang et al., 2024 |
| 2 | U-Net-SPM + gradient | 0.666 | 26.26 | 0.839 | 0.84 | ✓ Certified | SPM U-Net variant |
| 3 | SPM-Former + gradient | 0.661 | 26.1 | 0.835 | 0.83 | ✓ Certified | Chen et al., NanoLett 2024 |
| 4 | E2E-BTR + gradient | 0.612 | 23.45 | 0.748 | 0.89 | ✓ Certified | Kossler et al., Sci. Rep. 2022 |
| 5 | ScoreSPM + gradient | 0.608 | 23.54 | 0.752 | 0.86 | ✓ Certified | Wei et al., 2025 |
| 6 | Reg-Deconv + gradient | 0.565 | 21.88 | 0.685 | 0.86 | ✓ Certified | Dongmo et al., 2000 |
| 7 | BTR + gradient | 0.532 | 20.58 | 0.626 | 0.88 | ✓ Certified | Villarrubia, JRNIST 1997 |
| 8 | DeepSPM + gradient | 0.530 | 20.71 | 0.632 | 0.85 | ✓ Certified | Alldritt et al., Commun. Phys. 2020 |
| 9 | MLE Reconstruction + gradient | 0.529 | 20.47 | 0.621 | 0.88 | ✓ Certified | Classical statistical method |
| 10 | TV-Deconvolution + gradient | 0.478 | 19.39 | 0.569 | 0.78 | ✓ Certified | TV regularization for SPM |
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%