Dev

Laser-Induced Breakdown Spectroscopy (LIBS) Imaging — Dev Tier

(5 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
laser_energy_fluctuation -2.4 – 3.6 -
matrix_effect -7.2 – 10.8 -
self_absorption_correction -4.8 – 7.2 -
crater_to_crater_variation -3.6 – 5.4 -

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 SpectraFormer + gradient 0.723 28.74 0.895 0.89 ✓ Certified Spectroscopy transformer, 2024
2 DiffusionSpectra + gradient 0.658 26.06 0.834 0.82 ✓ Certified Zhang et al., 2024
3 Cascade-UNet + gradient 0.635 24.48 0.785 0.88 ✓ Certified Physics-informed UNet, 2025
4 ScoreSpectra + gradient 0.630 24.67 0.792 0.83 ✓ Certified Wei et al., 2025
5 SVD + gradient 0.602 23.6 0.754 0.82 ✓ Certified Singular Value Decomposition
6 SG-ALS + gradient 0.593 23.02 0.732 0.85 ✓ Certified Savitzky-Golay + ALS baseline
7 PnP-DnCNN + gradient 0.575 22.58 0.714 0.82 ✓ Certified Zhang et al., 2017
8 Baseline Correction + gradient 0.572 22.53 0.712 0.81 ✓ Certified Polynomial fitting baseline
9 CDAE + gradient 0.551 21.26 0.657 0.88 ✓ Certified Zhang et al., Sensors 2024
10 PINN-Spectra + gradient 0.546 20.99 0.645 0.89 ✓ Certified Physics-informed neural network
11 U-Net-Spectra + gradient 0.492 19.26 0.563 0.87 ✓ Certified Spectral U-Net variant

Visible Data Fields

y H_ideal spec_ranges

Dataset

Format: HDF5
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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