Hidden

Laser-Induced Breakdown Spectroscopy (LIBS) Imaging — 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
laser_energy_fluctuation -1.4 – 4.6 -
matrix_effect -4.2 – 13.8 -
self_absorption_correction -2.8 – 9.2 -
crater_to_crater_variation -2.1 – 6.9 -

Hidden Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 SpectraFormer + gradient 0.665 26.8 0.853 0.78 ✓ Certified Spectroscopy transformer, 2024
2 DiffusionSpectra + gradient 0.594 23.87 0.764 0.75 ✓ Certified Zhang et al., 2024
3 Cascade-UNet + gradient 0.586 22.74 0.721 0.85 ✓ Certified Physics-informed UNet, 2025
4 SVD + gradient 0.581 22.79 0.723 0.82 ✓ Certified Singular Value Decomposition
5 SG-ALS + gradient 0.542 21.08 0.649 0.86 ✓ Certified Savitzky-Golay + ALS baseline
6 ScoreSpectra + gradient 0.541 21.82 0.682 0.75 ✓ Certified Wei et al., 2025
7 Baseline Correction + gradient 0.539 21.03 0.647 0.85 ✓ Certified Polynomial fitting baseline
8 PnP-DnCNN + gradient 0.496 19.32 0.566 0.88 ✓ Certified Zhang et al., 2017
9 CDAE + gradient 0.489 19.11 0.555 0.88 ✓ Certified Zhang et al., Sensors 2024
10 PINN-Spectra + gradient 0.460 18.8 0.54 0.78 ✓ Certified Physics-informed neural network
11 U-Net-Spectra + gradient 0.444 18.13 0.506 0.8 ✓ Certified Spectral U-Net variant

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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