Hidden
Scanning Acoustic Microscopy (SAM) — 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 |
|---|---|---|
| coupling_medium_speed | 1470.2 – 1512.2 | m/s |
| focus_depth_error | -2.8 – 9.2 | um |
| lens_aberration | -0.028 – 0.092 | waves |
| gate_position_error | -0.7 – 2.3 | - |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | PINN-SAM + gradient | 0.615 | 24.58 | 0.789 | 0.77 | ✓ Certified | Guo et al., IEEE UFFC 71:340, 2024 |
| 2 | AcousticFormer + gradient | 0.612 | 24.62 | 0.79 | 0.75 | ✓ Certified | Zhu et al., Ultrasonics 138:107212, 2024 |
| 3 | DiffusionSAM + gradient | 0.574 | 22.21 | 0.699 | 0.86 | ✓ Certified | Score-based diffusion for SAM reconstruction, 2024 |
| 4 | PnP-ADMM + gradient | 0.541 | 21.01 | 0.646 | 0.86 | ✓ Certified | Venkatakrishnan et al., IEEE GlobalSIP 2013 |
| 5 | SAM-Net + gradient | 0.501 | 20.34 | 0.615 | 0.76 | ✓ Certified | Guo et al., Ultrasonics 122:106679, 2022 |
| 6 | Wiener Deconv + gradient | 0.451 | 18.7 | 0.535 | 0.75 | ✓ Certified | Zinin et al., J. Appl. Phys. 1997 |
| 7 | SAFT + gradient | 0.451 | 18.7 | 0.535 | 0.75 | ✓ Certified | Schickert et al., NDT&E Int. 36:339, 2003 |
| 8 | Self-Sup Deconv + gradient | 0.426 | 17.87 | 0.494 | 0.75 | ✓ Certified | He et al., IEEE Trans. Instrum. Meas. 73, 2024 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%