Public

X-ray Radiography — Public Tier

(3 scenes)

Full-access development tier with all data visible.

What you get

Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.

How to use

Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.

What to submit

Reconstructed signals (x_hat) and corrected spec as HDF5.

Parameter Specifications

✓

True spec visible — use these exact values for Scenario III oracle reconstruction.

Parameter Spec Range True Value Unit
source_dist -5.0 – 10.0 2.5 mm
beam_hardening -0.02 – 0.04 0.01
scatter -0.05 – 0.1 0.025

InverseNet Baseline Scores

Method: CPU_baseline — Mismatch parameter: nominal

Scenario I (Ideal)

14.88 dB

SSIM 0.2885

Scenario II (Mismatch)

11.68 dB

SSIM 0.0479

Scenario III (Oracle)

14.89 dB

SSIM 0.1290

Per-scene breakdown (4 scenes)
Scene PSNR I SSIM I PSNR II SSIM II PSNR III SSIM III
scene_00 14.80 0.2898 11.69 0.0458 14.77 0.1268
scene_01 15.05 0.2857 11.70 0.0477 14.91 0.1282
scene_02 14.99 0.2846 11.68 0.0506 14.96 0.1312
scene_03 14.69 0.2938 11.67 0.0475 14.91 0.1299

Public Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 Score-CT + gradient 0.865 38.33 0.983 0.94 ✓ Certified Song et al., NeurIPS 2024
2 DOLCE + gradient 0.846 36.69 0.977 0.94 ✓ Certified Liu et al., ICCV 2023
3 DiffusionCT + gradient 0.842 36.91 0.978 0.91 ✓ Certified Kazemi et al., ECCV 2024
4 CTFormer + gradient 0.840 36.9 0.978 0.9 ✓ Certified Li et al., ICCV 2024
5 CT-ViT + gradient 0.836 36.69 0.977 0.89 ✓ Certified Guo et al., NeurIPS 2024
6 Learned Primal-Dual + gradient 0.826 35.37 0.97 0.92 ✓ Certified Adler & Oktem, IEEE TMI 2018
7 DuDoTrans + gradient 0.818 35.07 0.968 0.9 ✓ Certified Wang et al., MLMIR 2022
8 FBPConvNet + gradient 0.793 33.32 0.955 0.89 ✓ Certified Jin et al., IEEE TIP 2017
9 RED-CNN + gradient 0.786 32.21 0.945 0.93 ✓ Certified Chen et al., IEEE TMI 2017
10 PnP-DnCNN + gradient 0.764 31.5 0.937 0.87 ✓ Certified Zhang et al., IEEE TIP 2017
11 PnP-ADMM + gradient 0.748 30.33 0.922 0.88 ✓ Certified Venkatakrishnan et al., IEEE GlobalSIP 2013
12 TV-ADMM + gradient 0.711 28.23 0.886 0.87 ✓ Certified Sidky et al., Phys. Med. Biol. 2008
13 FBP + gradient 0.643 24.57 0.788 0.91 ✓ Certified Kak & Slaney, IEEE Press 1988

Visible Data Fields

y H_ideal spec_ranges x_true true_spec

Dataset

Format: HDF5
Scenes: 3

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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