Public

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

(5 scenes)

Full-access development tier with all data visible.

What you get

Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.

How to use

Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.

What to submit

Reconstructed signals (x_hat) and corrected spec as HDF5.

Parameter Specifications

✓

True spec visible — use these exact values for Scenario III oracle reconstruction.

Parameter Spec Range True Value Unit
laser_energy_fluctuation -2.0 – 4.0 1.0 -
matrix_effect -6.0 – 12.0 3.0 -
self_absorption_correction -4.0 – 8.0 2.0 -
crater_to_crater_variation -3.0 – 6.0 1.5 -

InverseNet Baseline Scores

Method: CPU_baseline — Mismatch parameter: nominal

Scenario I (Ideal)

20.86 dB

SSIM 0.5556

Scenario II (Mismatch)

17.94 dB

SSIM 0.2739

Scenario III (Oracle)

21.09 dB

SSIM 0.4477

Per-scene breakdown (4 scenes)
Scene PSNR I SSIM I PSNR II SSIM II PSNR III SSIM III
scene_00 22.20 0.5293 20.35 0.2280 21.93 0.3469
scene_01 22.95 0.6386 19.17 0.3063 21.54 0.4791
scene_02 17.62 0.5116 14.75 0.2820 20.16 0.5023
scene_03 20.65 0.5429 17.50 0.2793 20.72 0.4626

Public Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 Cascade-UNet + gradient 0.779 31.84 0.941 0.92 ✓ Certified Physics-informed UNet, 2025
2 DiffusionSpectra + gradient 0.766 31.35 0.935 0.89 ✓ Certified Zhang et al., 2024
3 SpectraFormer + gradient 0.762 31.49 0.937 0.86 ✓ Certified Spectroscopy transformer, 2024
4 PINN-Spectra + gradient 0.758 30.35 0.922 0.93 ✓ Certified Physics-informed neural network
5 CDAE + gradient 0.757 30.27 0.921 0.93 ✓ Certified Zhang et al., Sensors 2024
6 ScoreSpectra + gradient 0.726 28.85 0.898 0.89 ✓ Certified Wei et al., 2025
7 U-Net-Spectra + gradient 0.713 27.58 0.872 0.94 ✓ Certified Spectral U-Net variant
8 PnP-DnCNN + gradient 0.693 26.81 0.853 0.92 ✓ Certified Zhang et al., 2017
9 SVD + gradient 0.630 24.2 0.776 0.89 ✓ Certified Singular Value Decomposition
10 SG-ALS + gradient 0.573 22.02 0.691 0.88 ✓ Certified Savitzky-Golay + ALS baseline
11 Baseline Correction + gradient 0.570 21.76 0.68 0.9 ✓ Certified Polynomial fitting baseline

Visible Data Fields

y H_ideal spec_ranges x_true true_spec

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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