Search Results for author: Ander Biguri

Found 6 papers, 2 papers with code

Learned denoising with simulated and experimental low-dose CT data

no code implementations15 Aug 2024 Maximilian B. Kiss, Ander Biguri, Carola-Bibiane Schönlieb, K. Joost Batenburg, Felix Lucka

The study furthermore suggests the need for more sophisticated noise simulation approaches to bridge the gap between simulated and real-world data in CT image denoising applications and gives insights into the challenges and opportunities in leveraging simulated data for machine learning in computational imaging.

Computed Tomography (CT) Image Denoising

Continuous Learned Primal Dual

no code implementations3 May 2024 Christina Runkel, Ander Biguri, Carola-Bibiane Schönlieb

Neural ordinary differential equations (Neural ODEs) propose the idea that a sequence of layers in a neural network is just a discretisation of an ODE, and thus can instead be directly modelled by a parameterised ODE.

Computed Tomography (CT) CT Reconstruction

Navigating the challenges in creating complex data systems: a development philosophy

no code implementations21 Oct 2022 Sören Dittmer, Michael Roberts, Julian Gilbey, Ander Biguri, AIX-COVNET Collaboration, Jacobus Preller, James H. F. Rudd, John A. D. Aston, Carola-Bibiane Schönlieb

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder.

Philosophy

Numerically robust tetrahedron-based tomographic forward and backward projectors on parallel architectures

1 code implementation19 Aug 2019 Ander Biguri, Hossein Towsyfyan, Richard Boardman, Thomas Blumensath

X-ray tomographic reconstruction typically uses voxel basis functions to represent volumetric images.

Distributed, Parallel, and Cluster Computing

Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox

1 code implementation8 May 2019 Ander Biguri, Reuben Lindroos, Robert Bryll, Hossein Towsyfyan, Hans Deyhle, Richard Boardman, Mark Mavrogordato, Manjit Dosanjh, Steven Hancock, Thomas Blumensath

Tomographic image sizes keep increasing over time and while the GPUs that compute the tomographic reconstruction are also increasing in memory size, they are not doing so fast enough to reconstruct the largest datasets.

Distributed, Parallel, and Cluster Computing

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