Dev

Scanning Acoustic Microscopy (SAM) — 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
coupling_medium_speed 1463.2 – 1505.2 m/s
focus_depth_error -4.8 – 7.2 um
lens_aberration -0.048 – 0.072 waves
gate_position_error -1.2 – 1.8 -

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 AcousticFormer + gradient 0.708 28.64 0.894 0.82 ✓ Certified Zhu et al., Ultrasonics 138:107212, 2024
2 PINN-SAM + gradient 0.662 26.25 0.839 0.82 ✓ Certified Guo et al., IEEE UFFC 71:340, 2024
3 DiffusionSAM + gradient 0.655 25.91 0.829 0.82 ✓ Certified Score-based diffusion for SAM reconstruction, 2024
4 SAM-Net + gradient 0.591 22.95 0.729 0.85 ✓ Certified Guo et al., Ultrasonics 122:106679, 2022
5 PnP-ADMM + gradient 0.590 23.08 0.734 0.83 ✓ Certified Venkatakrishnan et al., IEEE GlobalSIP 2013
6 Self-Sup Deconv + gradient 0.549 21.38 0.663 0.85 ✓ Certified He et al., IEEE Trans. Instrum. Meas. 73, 2024
7 SAFT + gradient 0.511 20.14 0.605 0.84 ✓ Certified Schickert et al., NDT&E Int. 36:339, 2003
8 Wiener Deconv + gradient 0.504 20.09 0.603 0.81 ✓ Certified Zinin et al., J. Appl. Phys. 1997

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%
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