Dev

Raman Imaging / Microscopy — 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
spectral_calibration_shift -0.48 – 0.72 cm^-1
fluorescence_background -2.4 – 3.6 relative
laser_power_fluctuation -1.2 – 1.8 -
cosmic_ray_artifact -0.24 – 0.36 -

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 Cascade-UNet + gradient 0.677 26.7 0.851 0.85 ✓ Certified Physics-informed UNet, 2025
2 U-Net-Spectra + gradient 0.660 26.27 0.839 0.81 ✓ Certified Spectral U-Net variant
3 SVD + gradient 0.634 24.67 0.792 0.85 ✓ Certified Singular Value Decomposition
4 SpectraFormer + gradient 0.625 24.76 0.794 0.8 ✓ Certified Spectroscopy transformer, 2024
5 PINN-Spectra + gradient 0.609 23.9 0.765 0.82 ✓ Certified Physics-informed neural network
6 PnP-DnCNN + gradient 0.594 23.47 0.749 0.8 ✓ Certified Zhang et al., 2017
7 ScoreSpectra + gradient 0.590 23.32 0.743 0.8 ✓ Certified Wei et al., 2025
8 CDAE + gradient 0.567 21.75 0.679 0.89 ✓ Certified Zhang et al., Sensors 2024
9 Baseline Correction + gradient 0.556 21.79 0.681 0.83 ✓ Certified Polynomial fitting baseline
10 DiffusionSpectra + gradient 0.514 20.08 0.603 0.86 ✓ Certified Zhang et al., 2024
11 SG-ALS + gradient 0.511 19.97 0.597 0.86 ✓ Certified Savitzky-Golay + ALS baseline

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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