Public
Second Harmonic Generation (SHG) 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 |
|---|---|---|---|
| phase_matching_error | -1.0 – 2.0 | 0.5 | - |
| excitation_power_fluctuation | -2.0 – 4.0 | 1.0 | - |
| collection_na_mismatch | -0.02 – 0.04 | 0.01 | - |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
13.27 dB
SSIM 0.3991
Scenario II (Mismatch)
11.88 dB
SSIM 0.2385
Scenario III (Oracle)
19.75 dB
SSIM 0.4579
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 17.02 | 0.3838 | 14.94 | 0.2440 | 20.04 | 0.5013 |
| scene_01 | 14.23 | 0.2969 | 12.92 | 0.2098 | 19.23 | 0.5480 |
| scene_02 | 8.52 | 0.3871 | 8.01 | 0.2308 | 20.11 | 0.3417 |
| scene_03 | 13.30 | 0.5284 | 11.66 | 0.2694 | 19.61 | 0.4408 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | ScoreMicro + gradient | 0.829 | 36.32 | 0.975 | 0.88 | ✓ Certified | Wei et al., ECCV 2025 |
| 2 | DiffDeconv + gradient | 0.825 | 36.29 | 0.975 | 0.86 | ✓ Certified | Huang et al., NeurIPS 2024 |
| 3 | Restormer+ + gradient | 0.816 | 34.74 | 0.966 | 0.91 | ✓ Certified | Zamir et al., ICCV 2024 |
| 4 | DeconvFormer + gradient | 0.812 | 34.6 | 0.965 | 0.9 | ✓ Certified | Chen et al., CVPR 2024 |
| 5 | ResUNet + gradient | 0.795 | 33.27 | 0.955 | 0.9 | ✓ Certified | DeCelle et al., Nat. Methods 2021 |
| 6 | Restormer + gradient | 0.792 | 32.9 | 0.952 | 0.91 | ✓ Certified | Zamir et al., CVPR 2022 |
| 7 | U-Net + gradient | 0.785 | 32.69 | 0.95 | 0.89 | ✓ Certified | Ronneberger et al., MICCAI 2015 |
| 8 | CARE + gradient | 0.773 | 31.6 | 0.938 | 0.91 | ✓ Certified | Weigert et al., Nat. Methods 2018 |
| 9 | PnP-DnCNN + gradient | 0.750 | 29.82 | 0.914 | 0.93 | ✓ Certified | Zhang et al., IEEE TIP 2017 |
| 10 | PnP-FISTA + gradient | 0.738 | 29.23 | 0.904 | 0.92 | ✓ Certified | Bai et al., 2020 |
| 11 | TV-Deconvolution + gradient | 0.718 | 27.85 | 0.878 | 0.94 | ✓ Certified | Rudin et al., Phys. A 1992 |
| 12 | Richardson-Lucy + gradient | 0.675 | 25.9 | 0.829 | 0.92 | ✓ Certified | Richardson, JOSA 1972 / Lucy, AJ 1974 |
| 13 | Wiener Filter + gradient | 0.664 | 25.5 | 0.818 | 0.91 | ✓ Certified | Analytical 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%