Dev

Lattice Light-Sheet 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
lattice_period_error -1.2 – 1.8 relative
dithering_range -0.15 – 0.15 -
sheet_na_error -0.012 – 0.018 -
excitation_psf_sidelobe -2.4 – 3.6 relative

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 DeconvFormer + gradient 0.775 33.46 0.957 0.79 ✓ Certified Chen et al., CVPR 2024
2 Restormer + gradient 0.759 30.92 0.93 0.89 ✓ Certified Zamir et al., CVPR 2022
3 Restormer+ + gradient 0.743 30.25 0.921 0.86 ✓ Certified Zamir et al., ICCV 2024
4 ScoreMicro + gradient 0.728 29.24 0.904 0.87 ✓ Certified Wei et al., ECCV 2025
5 DiffDeconv + gradient 0.699 28.16 0.884 0.82 ✓ Certified Huang et al., NeurIPS 2024
6 ResUNet + gradient 0.687 27.4 0.868 0.83 ✓ Certified DeCelle et al., Nat. Methods 2021
7 TV-Deconvolution + gradient 0.682 26.94 0.857 0.85 ✓ Certified Rudin et al., Phys. A 1992
8 U-Net + gradient 0.667 26.6 0.848 0.81 ✓ Certified Ronneberger et al., MICCAI 2015
9 Wiener Filter + gradient 0.654 26.04 0.833 0.8 ✓ Certified Analytical baseline
10 CARE + gradient 0.642 24.71 0.793 0.89 ✓ Certified Weigert et al., Nat. Methods 2018
11 PnP-FISTA + gradient 0.642 24.97 0.801 0.86 ✓ Certified Bai et al., 2020
12 Richardson-Lucy + gradient 0.641 24.99 0.802 0.85 ✓ Certified Richardson, JOSA 1972 / Lucy, AJ 1974
13 PnP-DnCNN + gradient 0.629 25.01 0.803 0.79 ✓ Certified Zhang et al., IEEE TIP 2017

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%
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