Hidden
SPC-Kronecker — Hidden Tier
(20 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 |
|---|---|---|
| gain_decay_alpha | 0.0035 – 0.0125 | 1/measurement |
| noise_sigma | 0.02 – 0.06 |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | PnP-DRUNet + blind cal | 0.687 | 24.75 | 0.776 | 0.9 | ✓ Certified | InverseNet Scenario IV |
| 2 | FISTA-TV (tuned) + blind cal | 0.671 | 24.5 | 0.73 | 0.91 | ✓ Certified | InverseNet Scenario IV |
| 3 | FISTA-TV (paper) + blind cal | 0.665 | 24.24 | 0.722 | 0.91 | ✓ Certified | InverseNet Scenario IV |
| 4 | HATNet + FISTA-TV + blind cal | 0.665 | 24.53 | 0.72 | 0.9 | ✓ Certified | InverseNet Scenario IV |
| 5 | PnP-BM3D + blind cal | 0.580 | 20.53 | 0.561 | 0.96 | ✓ Certified | InverseNet Scenario IV |
| 6 | ISTA-Net + blind cal | 0.509 | 19.99 | 0.385 | 0.98 | ✓ Certified | InverseNet Scenario IV |
Dataset
Scenes: 20
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%