Dev

Coherent Anti-Stokes Raman (CARS) 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
pump_stokes_frequency_offset -1.2 – 1.8 cm^-1
non_resonant_background -12.0 – 18.0 -
chirp_mismatch -120.0 – 180.0 fs^2

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 FMDiff-CARS + gradient 0.764 31.65 0.939 0.86 ✓ Certified Li et al., NeurIPS 2024
2 SpecFormer-CARS + gradient 0.751 31.82 0.941 0.78 ✓ Certified Liao et al., Light Sci. Appl. 2023
3 ResNet-CARS + gradient 0.731 29.74 0.913 0.84 ✓ Certified Ying et al., Optica 2022
4 Diff-CARS + gradient 0.703 28.49 0.891 0.81 ✓ Certified Zhang et al., Nat. Methods 2024
5 PINN-CARS + gradient 0.665 26.66 0.85 0.79 ✓ Certified Bae et al., ACS Photonics 2021
6 CNN-NRB + gradient 0.626 24.59 0.789 0.82 ✓ Certified Houhou et al., Chem. Sci. 2020
7 U-Net-CARS + gradient 0.608 23.22 0.74 0.9 ✓ Certified Manifold et al., Nat. Mach. Intell. 2021
8 MEM-CARS + gradient 0.601 23.25 0.741 0.86 ✓ Certified Rinia et al., J. Raman Spectrosc. 2008
9 KK-Retrieval + gradient 0.575 22.64 0.717 0.81 ✓ Certified Liu et al., Opt. Express 2009

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 Coherent Anti-Stokes Raman (CARS) Microscopy