Dev
Adaptive Optics (AO) Imaging — 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 |
|---|---|---|
| dm_actuator_gain | 0.976 – 1.036 | - |
| wfs_centroid_bias | -0.048 – 0.072 | px |
| fried_parameter_r0 | 0.126 – 0.186 | m |
| servo_lag | -0.48 – 0.72 | ms |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | AO-ViT + gradient | 0.716 | 28.32 | 0.887 | 0.89 | ✓ Certified | Vision transformer for AO, 2024 |
| 2 | AO-Transformer + gradient | 0.679 | 27.3 | 0.865 | 0.8 | ✓ Certified | Wavefront sensing transformer, 2023 |
| 3 | DiffusionAO + gradient | 0.654 | 25.11 | 0.806 | 0.9 | ✓ Certified | Score-based diffusion for wavefront reconstruction, 2024 |
| 4 | WFNet + gradient | 0.628 | 24.59 | 0.789 | 0.83 | ✓ Certified | Nishizaki et al., Opt. Express 2019 |
| 5 | LIFT-Net + gradient | 0.622 | 24.03 | 0.77 | 0.87 | ✓ Certified | Orban de Xivry et al., MNRAS 2021 |
| 6 | PnP-ADMM (WF) + gradient | 0.533 | 21.1 | 0.65 | 0.81 | ✓ Certified | Venkatakrishnan et al., 2013 |
| 7 | Fried Estimator + gradient | 0.512 | 19.96 | 0.597 | 0.87 | ✓ Certified | Fried, JOSA 1977 |
| 8 | Zernike LS + gradient | 0.487 | 19.72 | 0.585 | 0.78 | ✓ Certified | Noll, JOSA 1976 |
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%