Public
Scanning Acoustic Microscopy (SAM) — Public Tier
(3 scenes)Full-access development tier with all data visible.
What you get
Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.
How to use
Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.
What to submit
Reconstructed signals (x_hat) and corrected spec as HDF5.
Parameter Specifications
✓
True spec visible — use these exact values for Scenario III oracle reconstruction.
| Parameter | Spec Range | True Value | Unit |
|---|---|---|---|
| coupling_medium_speed | 1466.0 – 1508.0 | 1487.0 | m/s |
| focus_depth_error | -4.0 – 8.0 | 2.0 | um |
| lens_aberration | -0.04 – 0.08 | 0.02 | waves |
| gate_position_error | -1.0 – 2.0 | 0.5 | - |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
13.44 dB
SSIM 0.6187
Scenario II (Mismatch)
11.65 dB
SSIM 0.1994
Scenario III (Oracle)
19.78 dB
SSIM 0.1840
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 13.33 | 0.6172 | 12.92 | 0.1751 | 19.74 | 0.1812 |
| scene_01 | 13.64 | 0.6232 | 10.08 | 0.2297 | 19.89 | 0.1827 |
| scene_02 | 13.41 | 0.6172 | 11.17 | 0.2077 | 19.70 | 0.1891 |
| scene_03 | 13.36 | 0.6172 | 12.44 | 0.1851 | 19.79 | 0.1829 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | AcousticFormer + gradient | 0.792 | 32.62 | 0.949 | 0.93 | ✓ Certified | Zhu et al., Ultrasonics 138:107212, 2024 |
| 2 | DiffusionSAM + gradient | 0.782 | 32.23 | 0.945 | 0.91 | ✓ Certified | Score-based diffusion for SAM reconstruction, 2024 |
| 3 | PINN-SAM + gradient | 0.750 | 30.7 | 0.927 | 0.86 | ✓ Certified | Guo et al., IEEE UFFC 71:340, 2024 |
| 4 | Self-Sup Deconv + gradient | 0.725 | 29.18 | 0.903 | 0.86 | ✓ Certified | He et al., IEEE Trans. Instrum. Meas. 73, 2024 |
| 5 | SAM-Net + gradient | 0.688 | 26.64 | 0.849 | 0.91 | ✓ Certified | Guo et al., Ultrasonics 122:106679, 2022 |
| 6 | PnP-ADMM + gradient | 0.629 | 24.15 | 0.774 | 0.89 | ✓ Certified | Venkatakrishnan et al., IEEE GlobalSIP 2013 |
| 7 | Wiener Deconv + gradient | 0.525 | 20.12 | 0.604 | 0.91 | ✓ Certified | Zinin et al., J. Appl. Phys. 1997 |
| 8 | SAFT + gradient | 0.501 | 19.64 | 0.581 | 0.86 | ✓ Certified | Schickert et al., NDT&E Int. 36:339, 2003 |
Visible Data Fields
y
H_ideal
spec_ranges
x_true
true_spec
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%