Dev

Expansion Microscopy (ExM) — 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
expansion_factor 3.88 – 4.18 x
local_distortion -1.2 – 1.8 relative
anisotropic_expansion -0.72 – 1.08 xvsy

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 DiffExM + gradient 0.765 31.43 0.936 0.88 ✓ Certified Gao et al., NeurIPS 2024
2 SwinExM + gradient 0.760 32.33 0.946 0.79 ✓ Certified Wang et al., Cell Syst. 2023
3 PhysExM + gradient 0.758 31.53 0.937 0.84 ✓ Certified Chen et al., Nat. Commun. 2024
4 TransExM + gradient 0.741 31.02 0.931 0.79 ✓ Certified Li et al., Nat. Methods 2022
5 DnCNN-ExM + gradient 0.650 25.68 0.823 0.82 ✓ Certified Zhao et al., Nat. Methods 2019
6 DeepInterp-ExM + gradient 0.632 24.7 0.792 0.84 ✓ Certified Lecoq et al., Nat. Methods 2021
7 RL-ExM + gradient 0.602 23.86 0.764 0.79 ✓ Certified Richardson, J. Opt. Soc. Am. 1972
8 Deconv-Exp + gradient 0.554 21.72 0.678 0.83 ✓ Certified Chen et al., Science 2015
9 TV-ExM + gradient 0.455 18.03 0.501 0.87 ✓ Certified Rudin et al., Physica D 1992

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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