3 citations · 5 across the 5 of their papers we have counts for
6 papers
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…
Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements
Jennifer Maier, Marlies Nitschke, Jang-Hwan Choi +4
Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compe…
Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee
Jennifer Maier, Marlies Nitschke, Jang-Hwan Choi +4
Involuntary motion during weight-bearing cone-beam computed tomography (CT) scans of the knee causes artifacts in the reconstructed volumes making them unusable for clinical diagno…
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte +87
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new versi…
Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT
Jennifer Maier, Luis Carlos Rivera Monroy, Christopher Syben +7
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis. A precise segmentation of the cartila…
Precision Learning: Towards Use of Known Operators in Neural Networks
Andreas Maier, Frank Schebesch, Christopher Syben +4
In this paper, we consider the use of prior knowledge within neural networks. In particular, we investigate the effect of a known transform within the mapping from input data space…