Public
Stimulated Raman Scattering (SRS) Microscopy — 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 |
|---|---|---|---|
| lock_in_phase_error | -2.0 – 4.0 | 1.0 | deg |
| cross_phase_modulation | -1.0 – 2.0 | 0.5 | - |
| laser_intensity_noise_(rin) | -152.0 – -146.0 | -149.0 | dBc/Hz |
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 | 31.86 | 0.941 | 0.92 | ✓ Certified | Physics-informed UNet, 2025 |
| 2 | PINN-Spectra + gradient | 0.754 | 30.87 | 0.929 | 0.87 | ✓ Certified | Physics-informed neural network |
| 3 | SpectraFormer + gradient | 0.737 | 29.05 | 0.901 | 0.93 | ✓ Certified | Spectroscopy transformer, 2024 |
| 4 | DiffusionSpectra + gradient | 0.737 | 29.03 | 0.901 | 0.93 | ✓ Certified | Zhang et al., 2024 |
| 5 | ScoreSpectra + gradient | 0.736 | 28.96 | 0.9 | 0.93 | ✓ Certified | Wei et al., 2025 |
| 6 | CDAE + gradient | 0.725 | 28.62 | 0.893 | 0.91 | ✓ Certified | Zhang et al., Sensors 2024 |
| 7 | U-Net-Spectra + gradient | 0.683 | 26.41 | 0.843 | 0.91 | ✓ Certified | Spectral U-Net variant |
| 8 | PnP-DnCNN + gradient | 0.656 | 25.22 | 0.809 | 0.9 | ✓ Certified | Zhang et al., 2017 |
| 9 | Baseline Correction + gradient | 0.621 | 23.42 | 0.747 | 0.94 | ✓ Certified | Polynomial fitting baseline |
| 10 | SVD + gradient | 0.594 | 22.92 | 0.728 | 0.87 | ✓ Certified | Singular Value Decomposition |
| 11 | SG-ALS + gradient | 0.572 | 22.01 | 0.69 | 0.88 | ✓ Certified | Savitzky-Golay + ALS 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%