1 citations · 1 across the 5 of their papers we have counts for
6 papers
Improving the Generalisation of Learned Reconstruction Frameworks
Emilien Valat, Ozan Öktem
Ensuring proper generalization is a critical challenge in applying data-driven methods for solving inverse problems in imaging, as neural networks reconstructing an image must perf…
TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets
Ander Biguri, Tomoyuki Sadakane, Reuben Lindroos +11
Computed Tomography (CT) has been widely adopted in medicine and it is increasingly being used in scientific and industrial applications. Parallelly, research in different mathemat…
Self-Supervised Denoiser Framework
Emilien Valat, Andreas Hauptmann, Ozan Öktem
Reconstructing images using Computed Tomography (CT) in an industrial context leads to specific challenges that differ from those encountered in other areas, such as clinical CT. I…
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning
Emilien Valat, Loth Valat
Machine-learning methods rely on sufficiently large dataset to learn data distributions. They are widely used in research in X-Ray Computed Tomography, from low-dose scan denoising…
Data-Driven Interpolation for Super-Scarce X-Ray Computed Tomography
Emilien Valat, Katayoun Farrahi, Thomas Blumensath
We address the problem of reconstructing X-Ray tomographic images from scarce measurements by interpolating missing acquisitions using a self-supervised approach. To do so, we trai…
Sinogram Enhancement with Generative Adversarial Networks using Shape Priors
Emilien Valat, Katayoun Farrahi, Thomas Blumensath
Compensating scarce measurements by inferring them from computational models is a way to address ill-posed inverse problems. We tackle Limited Angle Tomography by completing the se…