Hidden

Raman Imaging / Microscopy — Hidden Tier

(5 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
spectral_calibration_shift -0.28 – 0.92 cm^-1
fluorescence_background -1.4 – 4.6 relative
laser_power_fluctuation -0.7 – 2.3 -
cosmic_ray_artifact -0.14 – 0.46 -

Hidden Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 U-Net-Spectra + gradient 0.619 24.64 0.791 0.78 ✓ Certified Spectral U-Net variant
2 Cascade-UNet + gradient 0.582 23.27 0.742 0.76 ✓ Certified Physics-informed UNet, 2025
3 SVD + gradient 0.570 22.89 0.727 0.75 ✓ Certified Singular Value Decomposition
4 SpectraFormer + gradient 0.561 22.2 0.698 0.8 ✓ Certified Spectroscopy transformer, 2024
5 PnP-DnCNN + gradient 0.543 21.82 0.682 0.76 ✓ Certified Zhang et al., 2017
6 ScoreSpectra + gradient 0.532 20.7 0.632 0.86 ✓ Certified Wei et al., 2025
7 CDAE + gradient 0.520 20.72 0.633 0.8 ✓ Certified Zhang et al., Sensors 2024
8 PINN-Spectra + gradient 0.506 20.56 0.625 0.75 ✓ Certified Physics-informed neural network
9 Baseline Correction + gradient 0.505 20.25 0.611 0.79 ✓ Certified Polynomial fitting baseline
10 DiffusionSpectra + gradient 0.489 19.64 0.581 0.8 ✓ Certified Zhang et al., 2024
11 SG-ALS + gradient 0.476 19.41 0.57 0.77 ✓ Certified Savitzky-Golay + ALS baseline

Dataset

Scenes: 5

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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