Dev

Spinning Disk Confocal Microscopy — 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
pinhole_crosstalk -3.6 – 5.4 -
disk_rotation_wobble -0.24 – 0.36 px
illumination_non_uniformity -2.4 – 3.6 -

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 Restormer+ + gradient 0.778 32.51 0.948 0.87 ✓ Certified Zamir et al., ICCV 2024
2 DeconvFormer + gradient 0.773 31.88 0.941 0.89 ✓ Certified Chen et al., CVPR 2024
3 Restormer + gradient 0.727 28.91 0.899 0.89 ✓ Certified Zamir et al., CVPR 2022
4 ScoreMicro + gradient 0.725 29.17 0.903 0.86 ✓ Certified Wei et al., ECCV 2025
5 ResUNet + gradient 0.722 29.33 0.906 0.83 ✓ Certified DeCelle et al., Nat. Methods 2021
6 U-Net + gradient 0.672 26.26 0.839 0.87 ✓ Certified Ronneberger et al., MICCAI 2015
7 DiffDeconv + gradient 0.670 26.33 0.841 0.85 ✓ Certified Huang et al., NeurIPS 2024
8 CARE + gradient 0.664 25.6 0.821 0.9 ✓ Certified Weigert et al., Nat. Methods 2018
9 PnP-FISTA + gradient 0.649 25.54 0.819 0.83 ✓ Certified Bai et al., 2020
10 PnP-DnCNN + gradient 0.648 25.68 0.823 0.81 ✓ Certified Zhang et al., IEEE TIP 2017
11 TV-Deconvolution + gradient 0.643 24.93 0.8 0.87 ✓ Certified Rudin et al., Phys. A 1992
12 Wiener Filter + gradient 0.638 24.63 0.79 0.88 ✓ Certified Analytical baseline
13 Richardson-Lucy + gradient 0.623 24.42 0.783 0.83 ✓ Certified Richardson, JOSA 1972 / Lucy, AJ 1974

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%
Back to Spinning Disk Confocal Microscopy