Public
Confocal Laser Endomicroscopy (CLE) — Public Tier
(3 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 |
|---|---|---|---|
| fiber_bundle_honeycomb_pattern | -0.15 – 0.15 | 0.0 | - |
| motion_artifact | -2.0 – 4.0 | 1.0 | px/frame |
| fluorescein_concentration_variation | 0.6 – 1.8 | 1.2 | relative |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
14.88 dB
SSIM 0.2885
Scenario II (Mismatch)
11.40 dB
SSIM 0.0410
Scenario III (Oracle)
14.64 dB
SSIM 0.1177
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 14.80 | 0.2898 | 11.38 | 0.0400 | 14.58 | 0.1169 |
| scene_01 | 15.05 | 0.2857 | 11.41 | 0.0409 | 14.66 | 0.1169 |
| scene_02 | 14.99 | 0.2846 | 11.40 | 0.0427 | 14.66 | 0.1192 |
| scene_03 | 14.69 | 0.2938 | 11.40 | 0.0405 | 14.65 | 0.1180 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DiffusionEndo + gradient | 0.840 | 37.39 | 0.98 | 0.87 | ✓ Certified | Li et al., Med. Image Anal. 2024 |
| 2 | SwinIR-CLE + gradient | 0.829 | 35.62 | 0.971 | 0.92 | ✓ Certified | Liang et al., ICCV 2021 (CLE) |
| 3 | Restormer-CLE + gradient | 0.825 | 35.96 | 0.973 | 0.88 | ✓ Certified | Zamir et al., CVPR 2022 (CLE) |
| 4 | PINN-CLE + gradient | 0.821 | 34.81 | 0.966 | 0.93 | ✓ Certified | Kang et al., Med. Phys. 2022 |
| 5 | U-Net-CLE + gradient | 0.791 | 32.58 | 0.949 | 0.93 | ✓ Certified | Andre et al., Med. Image Anal. 2011 (updated DL) |
| 6 | CARE-CLE + gradient | 0.783 | 32.27 | 0.946 | 0.91 | ✓ Certified | Weigert et al., Nat. Methods 2018 (CLE) |
| 7 | DnCNN-CLE + gradient | 0.723 | 28.57 | 0.892 | 0.9 | ✓ Certified | Zhang et al., IEEE TIP 2017 |
| 8 | BM3D-CLE + gradient | 0.665 | 26.02 | 0.833 | 0.86 | ✓ Certified | Dabov et al., IEEE TIP 2007 |
| 9 | NLM-Speckle + gradient | 0.609 | 23.49 | 0.75 | 0.87 | ✓ Certified | Buades et al., CVPR 2005 |
Visible Data Fields
y
H_ideal
spec_ranges
x_true
true_spec
Dataset
Format: HDF5
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%