PYRO-NN: Python Reconstruction Operators in Neural Networks

PYRO-NN: Python Reconstruction Operators in Neural Networks

30 April 2019

Christopher Syben

Bernhard Stimpel

Stefan B. Ploner

Papers citing "PYRO-NN: Python Reconstruction Operators in Neural Networks"

7 / 7 papers shown

Title
Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model Chengze Ye Linda-Sophie Schneider Yipeng Sun Mareike Thies Andreas Maier 39 0 0 20 Jan 2025
Optimizing CT Scan Geometries With and Without Gradients Mareike Thies Fabian Wagner Noah Maul Laura Pfaff Linda-Sophie Schneider Christopher Syben Andreas Maier 26 1 0 13 Feb 2023
Data Models for Dataset Drift Controls in Machine Learning With Optical Images Luis Oala Marco Aversa Gabriel Nobis Kurt Willis Yoan Neuenschwander ... E. Pomarico Wojciech Samek Roderick Murray-Smith Christoph Clausen B. Sanguinetti 28 5 0 04 Nov 2022
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer Theodor Cheslerean-Boghiu Felix C. Hofmann M. Schultheiss F. Pfeiffer Daniela Pfeiffer Tobias Lasser MedIm OOD 25 24 0 01 Jul 2022
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography Fabian Wagner Mareike Thies Mingxuan Gu Yixing Huang Sabrina Pechmann ... O. Aust S. Uderhardt G. Schett S. Christiansen Andreas Maier MedIm AI4CE 31 22 0 25 Jan 2022
Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network A. Sheshkus A. Chirvonaya D. Matveev D. Nikolaev Vladimir L. Arlazarov 11 8 0 04 Feb 2020
Learning with Known Operators reduces Maximum Training Error Bounds Andreas Maier Christopher Syben Bernhard Stimpel Tobias Würfl M. Hoffmann Frank Schebesch Weilin Fu L. Mill L. Kling S. Christiansen 30 108 0 03 Jul 2019