Hidden
Active Thermography (IR) — 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 |
|---|---|---|
| emissivity_error | 0.943 – 0.973 | - |
| heat_source_power_drift | 0.986 – 1.046 | - |
| background_temperature | 24.3 – 27.3 | C |
| integration_time_offset | -0.014 – 0.046 | s |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | ThermoFormer + gradient | 0.686 | 26.91 | 0.856 | 0.87 | ✓ Certified | Transformer for thermography reconstruction, 2024 |
| 2 | DiffusionThermo + gradient | 0.650 | 25.8 | 0.826 | 0.81 | ✓ Certified | Score-based diffusion for thermal imaging, 2024 |
| 3 | PINN-Thermo + gradient | 0.627 | 24.22 | 0.776 | 0.87 | ✓ Certified | Raissi et al. 2019; thermography extension 2024 |
| 4 | U-Net Thermo + gradient | 0.580 | 22.44 | 0.708 | 0.86 | ✓ Certified | Fang et al., IEEE Trans. Instrum. Meas. 2023 |
| 5 | PnP-ADMM + gradient | 0.565 | 22.09 | 0.694 | 0.83 | ✓ Certified | Venkatakrishnan et al., IEEE GlobalSIP 2013 |
| 6 | PCT + gradient | 0.495 | 19.42 | 0.571 | 0.86 | ✓ Certified | Maldague & Marinetti, J. Appl. Phys. 1996 |
| 7 | TSR + gradient | 0.466 | 19.19 | 0.559 | 0.75 | ✓ Certified | Shepard, Thermosense 2001; Shepard et al., Opt. Eng. 2003 |
| 8 | ThermoNet + gradient | 0.463 | 18.56 | 0.528 | 0.83 | ✓ Certified | Hu et al., NDT&E Int. 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%