Dev
Endoscopy — Dev Tier
(3 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 |
|---|---|---|
| fiber_coupling | -6.0 – 9.0 | % |
| core_spacing | -0.6 – 0.9 | μm |
| bending_loss | -0.36 – 0.54 | dB |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SwinEndo + gradient | 0.786 | 32.78 | 0.951 | 0.89 | ✓ Certified | Li et al., IEEE TMI 2023 |
| 2 | TransEndo + gradient | 0.774 | 32.61 | 0.949 | 0.84 | ✓ Certified | Wang et al., Med. Image Anal. 2022 |
| 3 | PhysEndo + gradient | 0.755 | 31.73 | 0.94 | 0.81 | ✓ Certified | Chen et al., Med. Image Anal. 2024 |
| 4 | DiffEndo + gradient | 0.749 | 30.13 | 0.919 | 0.9 | ✓ Certified | Gao et al., MICCAI 2024 |
| 5 | EndoSLAM-Net + gradient | 0.662 | 26.08 | 0.834 | 0.84 | ✓ Certified | Ozyoruk et al., Med. Image Anal. 2021 |
| 6 | BM3D-Endo + gradient | 0.649 | 24.99 | 0.802 | 0.89 | ✓ Certified | Dabov et al., IEEE TIP 2007 |
| 7 | DnCNN-Endo + gradient | 0.642 | 25.4 | 0.815 | 0.81 | ✓ Certified | Zhang et al., IEEE TIP 2017 |
| 8 | CLAHE-Endo + gradient | 0.597 | 23.42 | 0.747 | 0.82 | ✓ Certified | Zuiderveld, Graphics Gems IV 1994 |
| 9 | Histogram-Eq + gradient | 0.568 | 22.16 | 0.697 | 0.84 | ✓ Certified | Gonzalez & Woods, Digital Image Processing 2002 |
Visible Data Fields
y
H_ideal
spec_ranges
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%