News

Sept 2018: I’m organizing the international workshop Deep Learning and Inverse Problems 21-25 Jan 2019 in Stockholm.

Sept 2018: Banach Wasserstein GAN has been accepted for publication at NIPS.

Sept 2018: New preprint: Task adapted reconstruction for inverse problems.

Recent Posts

An introduction to some Machine Learning methods for image reconstruction.

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Instructions for installing and configuring Ubuntu 16.04 LTE on a PC with two GPUs.

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Recent Publications

More Publications

. Task adapted reconstruction for inverse problems. arXiv, 2018.

Preprint PDF

. Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction. arXiv, 2018.

Preprint PDF

. Data-driven Nonsmooth Optimization. arXiv, 2018.

Preprint PDF Code

. EDS tomographic reconstruction regularized by total nuclear variation joined with HAADF-STEM tomography. Ultramicroscopy, 2018.

. Banach Wasserstein GAN. arXiv, 2018.

Preprint PDF Code

. Learning to solve inverse problems using Wasserstein loss. NIPS workshop in optimal transport, 2017.

Preprint PDF Code

. Model based learning for accelerated, limited-view 3D photoacoustic tomography. IEEE - Transactions on Medical Imaging, 2017.

Preprint PDF Code

. Learned Primal-Dual Reconstruction. IEEE - Transactions on Medical Imaging, 2017.

Preprint PDF Code

. GPUMCI: a flexible platform for x-ray imaging on the GPU. Fully3D, 2017.

Recent & Upcoming Talks

Invited talk: Learned Iterative Reconstruction for CT
Jun 8, 2018
Invited talk: Learning to solve inverse problems with ODL
Jun 8, 2018
Contributed talk: Learned iterative reconstruction
Mar 9, 2018
Poster: Learning to solve inverse problems using Wasserstein Loss
Dec 9, 2017
Contributed talk: Learned forward operators: Variational regularization for black-box models
Oct 31, 2017
Invited talk: Learned iterative reconstruction schemes, theory and practice
Sep 18, 2017
ODL: A python library for inverse problems
Nov 15, 2015

Open Source

ODL

Operator Discretization Library (ODL) is a Python library that enables research in inverse problems on realistic or real data.

Minimal ML implementations

Some minimalistic implementations of generative models:

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