most citedUltra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography

30 citations · 34 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy

Mareike Thies, Fabian Wagner, Mingxuan Gu +10

Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are cha…

cs.CV2023

Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography

Fabian Wagner, Mareike Thies, Noah Maul +5

The diagnostic quality of computed tomography (CT) scans is usually restricted by the induced patient dose, scan speed, and image quality. Sparse-angle tomographic scans reduce rad…

eess.IV2022

Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising

Fabian Wagner, Mareike Thies, Laura Pfaff +10

Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usual…

eess.IV2022★ 4 cited

On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting

Fabian Wagner, Mareike Thies, Laura Pfaff +9

Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low…

eess.IV2022★ 30 cited

Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography

Fabian Wagner, Mareike Thies, Mingxuan Gu +9

Computed tomography is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution and radiation dose…