Hidden

STED — Hidden Tier

(5 scenes)

Fully blind server-side evaluation — no data download.

What you get

No data downloadable. Algorithm runs server-side on hidden measurements.

How to use

Package algorithm as Docker container / Python script. Submit via link.

What to submit

Containerized algorithm accepting y + H, outputting x_hat + corrected spec.

Parameter Specifications

🔒

True spec hidden — blind evaluation, only ranges available.

Parameter Spec Range Unit
depletion_power -7.0 – 23.0 %
donut_alignment -7.0 – 23.0 nm
saturation_intensity -5.6 – 18.4 %

Hidden Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 Restormer+ + gradient 0.751 31.97 0.942 0.77 ✓ Certified Zamir et al., ICCV 2024
2 DeconvFormer + gradient 0.721 29.02 0.901 0.85 ✓ Certified Chen et al., CVPR 2024
3 ScoreMicro + gradient 0.712 29.55 0.91 0.76 ✓ Certified Wei et al., ECCV 2025
4 Restormer + gradient 0.700 27.67 0.874 0.87 ✓ Certified Zamir et al., CVPR 2022
5 PnP-DnCNN + gradient 0.663 26.5 0.846 0.8 ✓ Certified Zhang et al., IEEE TIP 2017
6 ResUNet + gradient 0.657 25.45 0.816 0.88 ✓ Certified DeCelle et al., Nat. Methods 2021
7 DiffDeconv + gradient 0.656 26.31 0.841 0.78 ✓ Certified Huang et al., NeurIPS 2024
8 TV-Deconvolution + gradient 0.640 25.7 0.823 0.77 ✓ Certified Rudin et al., Phys. A 1992
9 PnP-FISTA + gradient 0.627 24.42 0.783 0.85 ✓ Certified Bai et al., 2020
10 Wiener Filter + gradient 0.618 24.61 0.79 0.78 ✓ Certified Analytical baseline
11 U-Net + gradient 0.599 23.91 0.765 0.77 ✓ Certified Ronneberger et al., MICCAI 2015
12 Richardson-Lucy + gradient 0.598 23.43 0.748 0.82 ✓ Certified Richardson, JOSA 1972 / Lucy, AJ 1974
13 CARE + gradient 0.579 22.49 0.711 0.85 ✓ Certified Weigert et al., Nat. Methods 2018

Dataset

Scenes: 5

Scoring Formula

0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)

PSNR: 40% SSIM: 40% Consistency: 20%
Back to STED