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Solving ill-posed inverse problems using iterative deep neural networks
KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).
KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).ORCID-id: 0000-0002-1118-6483
2017 (engelsk)Inngår i: Inverse Problems, ISSN 0266-5611, E-ISSN 1361-6420, Vol. 33, nr 12, artikkel-id 124007Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the 'gradient' component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512 x 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).

sted, utgiver, år, opplag, sider
Institute of Physics Publishing (IOPP), 2017. Vol. 33, nr 12, artikkel-id 124007
Emneord [en]
tomography, deep learning, gradient descent, regularization
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-219496DOI: 10.1088/1361-6420/aa9581ISI: 000416015300001Scopus ID: 2-s2.0-85038424472OAI: oai:DiVA.org:kth-219496DiVA, id: diva2:1163554
Merknad

QC 20171207

Tilgjengelig fra: 2017-12-07 Laget: 2017-12-07 Sist oppdatert: 2019-10-18bibliografisk kontrollert
Inngår i avhandling
1. Data-driven Methods in Inverse Problems
Åpne denne publikasjonen i ny fane eller vindu >>Data-driven Methods in Inverse Problems
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

In this thesis on data-driven methods in inverse problems we introduce several new methods to solve inverse problems using recent advancements in machine learning and specifically deep learning. The main goal has been to develop practically applicable methods, scalable to medical applications and with the ability to handle all the complexities associated with them.

In total, the thesis contains six papers. Some of them are focused on more theoretical questions such as characterizing the optimal solutions of reconstruction schemes or extending current methods to new domains, while others have focused on practical applicability. A significant portion of the papers also aim to bringing knowledge from the machine learning community into the imaging community, with considerable effort spent on translating many of the concepts. The papers have been published in a range of venues: machine learning, medical imaging and inverse problems.

The first two papers contribute to a class of methods now called learned iterative reconstruction where we introduce two ways of combining classical model driven reconstruction methods with deep neural networks. The next two papers look forward, aiming to address the question of "what do we want?" by proposing two very different but novel loss functions for training neural networks in inverse problems. The final papers dwelve into the statistical side, one gives a generalization of a class of deep generative models to Banach spaces while the next introduces two ways in which such methods can be used to perform Bayesian inversion at scale.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2019. s. 196
Serie
TRITA-SCI-FOU ; 2019;49
Emneord
Inverse Problems, Machine Learning, Tomography
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-262727 (URN)978-91-7873-334-7 (ISBN)
Disputas
2019-10-31, F3, Lindstedtsvägen26, KTH, Stockholm, 14:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Swedish Foundation for Strategic Research
Tilgjengelig fra: 2019-10-21 Laget: 2019-10-18 Sist oppdatert: 2019-10-21bibliografisk kontrollert

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