Dev
X-ray Fluorescence Tomography — 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 |
|---|---|---|
| self_absorption_correction | -7.2 – 10.8 | - |
| rotation_axis_offset | -0.72 – 1.08 | px |
| fluorescence_yield_error | -2.4 – 3.6 | - |
| dead_time_at_high_count_rate | -2.4 – 3.6 | - |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | CalibFormer + gradient | 0.707 | 27.74 | 0.875 | 0.9 | ✓ Certified | Transformer calibration, 2024 |
| 2 | MassSpecFormer + gradient | 0.657 | 25.65 | 0.822 | 0.86 | ✓ Certified | Mass spectrometry transformer, 2024 |
| 3 | Instrument-CNN + gradient | 0.639 | 25.28 | 0.811 | 0.81 | ✓ Certified | Instrument-specific CNN |
| 4 | ResNet-Calib + gradient | 0.629 | 24.42 | 0.783 | 0.86 | ✓ Certified | ResNet for calibration, 2022 |
| 5 | DiffusionInstrumentation + gradient | 0.622 | 24.11 | 0.772 | 0.86 | ✓ Certified | Zhang et al., 2024 |
| 6 | Calibration-Lookup + gradient | 0.620 | 23.92 | 0.766 | 0.87 | ✓ Certified | Look-up table calibration |
| 7 | Peak Fitting + gradient | 0.620 | 24.17 | 0.775 | 0.84 | ✓ Certified | Gaussian peak fitting |
| 8 | PnP-BM3D + gradient | 0.594 | 23.46 | 0.749 | 0.8 | ✓ Certified | Danielyan et al., 2012 |
| 9 | Deconv + gradient | 0.549 | 21.73 | 0.678 | 0.8 | ✓ Certified | Analytical baseline |
| 10 | PnP-NLM + gradient | 0.508 | 19.87 | 0.592 | 0.86 | ✓ Certified | Non-local means filter |
| 11 | ScoreInstrumentation + gradient | 0.461 | 18.29 | 0.514 | 0.86 | ✓ Certified | Wei et al., 2025 |
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%