Dev

Second Harmonic Generation (SHG) 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
phase_matching_error -1.2 – 1.8 -
excitation_power_fluctuation -2.4 – 3.6 -
collection_na_mismatch -0.024 – 0.036 -

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 Restormer+ + gradient 0.784 32.89 0.952 0.87 ✓ Certified Zamir et al., ICCV 2024
2 DeconvFormer + gradient 0.770 32.23 0.945 0.85 ✓ Certified Chen et al., CVPR 2024
3 ScoreMicro + gradient 0.753 31.3 0.935 0.83 ✓ Certified Wei et al., ECCV 2025
4 ResUNet + gradient 0.727 29.06 0.901 0.88 ✓ Certified DeCelle et al., Nat. Methods 2021
5 Restormer + gradient 0.727 28.93 0.899 0.89 ✓ Certified Zamir et al., CVPR 2022
6 DiffDeconv + gradient 0.694 27.97 0.88 0.81 ✓ Certified Huang et al., NeurIPS 2024
7 PnP-DnCNN + gradient 0.693 27.51 0.87 0.85 ✓ Certified Zhang et al., IEEE TIP 2017
8 CARE + gradient 0.691 27.07 0.86 0.88 ✓ Certified Weigert et al., Nat. Methods 2018
9 TV-Deconvolution + gradient 0.686 27.16 0.862 0.85 ✓ Certified Rudin et al., Phys. A 1992
10 U-Net + gradient 0.683 27.2 0.863 0.83 ✓ Certified Ronneberger et al., MICCAI 2015
11 PnP-FISTA + gradient 0.668 26.45 0.844 0.83 ✓ Certified Bai et al., 2020
12 Wiener Filter + gradient 0.651 25.94 0.83 0.8 ✓ Certified Analytical baseline
13 Richardson-Lucy + gradient 0.629 24.56 0.788 0.84 ✓ 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%
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