VMVW02
30 October 2017 to 3 November 2017
A key issue in image reconstruction, and in inverse problems as a whole, is the correct choice of image priors (or regularisation functionals) and data models (or fidelity terms) in a variational or Bayesian reconstruction model. Depending on the setup of the model, very different qualitative image reconstruction results are obtained. A setup of a variational imaging approach is influenced by the type of image one aims to reconstruct, as well as the way the image or data is acquired. The knowledge of the image properties -- such as the regularity of the image or present scales of image structures -- and the capability of modelling them, are crucial for an accurate setup of the image prior and as such for faithfully reconstructing the image contents. The image prior can have various forms, such as a regularisation term or a basis in which the image should be expanded. Sparsity plays a central role here. Sparsity promoting regularization is a widespread and very popular approach to solve inverse problems. Standard SPR methods like total variation (TV) or l1 regularization have been shown to be powerful tools to recover inverse problems solutions from a reduced amount of noisy measurements. Nevertheless, despite their ability of capturing important features such as discontinuities, these model-based regularizations are also well known to produce artefacts, such as the staircasing effect, if the measured data does not fit the corresponding model. An ideal SPR for a given application should be tailor-made, and reconstruct solutions one would expect rather than to create best fits to a standardized model.
The mechanism of the data acquisition process embodies the data model. This model explains how the data is related to the underlying image, containing information about the noise distribution, the amount of under-sampling and the physics of the image acquisition technique. Several strategies for deriving an optimal choice for an image enhancement approach have been considered in the literature. More heuristic approaches derive the model setup from the physics behind the acquisition process. Statistically grounded approaches are more data driven in the sense that they estimate or learn the noise and structure from the data itself. Adaptive regularisation approaches for instance are capable of adjusting the parameter values locally taking into account the noise level and the local scale of structures in the image. Moreover, machine learning methods, e.g. dictionary learning, are very powerful techniques to determine the correct basis in which an image should be reconstructed. Recent approaches in the community also propose to learn the imaging model by bilevel optimisation techniques. For gaining more insight into the reconstruction abilities of regularisers their analysis via singular vectors has also proven valuable in some recent works in the community.
Monday 30th October 2017 | |||
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09:00 to 09:40 | No Room Required | ||
09:40 to 09:50 | No Room Required | ||
09:50 to 10:40 |
James Nagy Emory University |
Room 1 | |
10:40 to 11:10 | No Room Required | ||
11:10 to 12:00 |
Eldad Haber University of British Columbia |
Room 1 | |
12:00 to 12:50 |
Christoph Brune Universiteit Twente |
Room 1 | |
12:50 to 14:00 | No Room Required | ||
14:00 to 14:50 |
Lars Ruthotto Emory University |
Room 1 | |
14:50 to 15:40 |
Gitta Kutyniok Technische Universität Berlin |
Room 1 | |
15:40 to 16:00 |
Eva-Maria Brinkmann Westfalische Wilhelms-Universitat Munster |
Room 1 | |
16:00 to 16:30 | No Room Required | ||
16:30 to 17:20 |
Joan Bruna New York University; University of California, Berkeley |
Room 1 | |
17:20 to 18:10 |
Justin Romberg Georgia Institute of Technology |
Room 1 | |
18:10 to 19:10 | No Room Required |
Tuesday 31st October 2017 | |||
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09:00 to 09:50 |
Stacey Levine Duquesne University |
Room 1 | |
09:50 to 10:40 |
Ozan Öktem KTH - Royal Institute of Technology; Karolinska Institute |
Room 1 | |
10:40 to 11:10 | No Room Required | ||
11:10 to 12:00 |
Lior Horesh IBM Research |
Room 1 | |
12:00 to 12:50 |
Martin Benning University of Cambridge |
Room 1 | |
12:50 to 14:00 | No Room Required | ||
14:00 to 14:50 |
Alfred Hero University of Michigan |
Room 1 | |
14:50 to 15:40 |
Francis Bach INRIA Paris - Rocquencourt; ENS - Paris |
Room 1 | |
15:40 to 16:00 |
Jonas Adler KTH - Royal Institute of Technology |
Room 1 | |
16:00 to 16:30 | No Room Required | ||
16:30 to 17:20 |
Julianne Chung Virginia Polytechnic Institute and State University |
Room 1 | |
17:20 to 18:10 |
Andreas Hauptmann University College London |
Room 1 | |
Wednesday 1st November 2017 | |||
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09:00 to 09:50 |
Mila Nikolova CNRS (Centre national de la recherche scientifique); ENS de Cachan |
Room 1 | |
09:50 to 10:40 |
Xavier Bresson Nanyang Technological University |
Room 1 | |
10:40 to 11:10 | No Room Required | ||
11:10 to 12:00 |
Julie Delon Université Paris Descartes |
Room 1 | |
12:00 to 12:50 |
Bangti Jin University College London |
Room 1 | |
12:50 to 14:00 | No Room Required | ||
14:00 to 17:00 | No Room Required |
Thursday 2nd November 2017 | |||
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09:00 to 09:50 |
Silvia Gazzola University of Bath |
Room 1 | |
09:50 to 10:40 |
Pierre Weiss Université de Toulouse |
Room 1 | |
10:40 to 11:10 | No Room Required | ||
11:10 to 12:00 |
Anders Hansen University of Cambridge |
Room 1 | |
12:00 to 12:50 |
Josiane Zerubia INRIA Sophia Antipolis |
Room 1 | |
12:50 to 14:00 | No Room Required | ||
14:00 to 14:50 |
Mario Figueiredo Universidade de Lisboa |
Room 1 | |
14:50 to 15:40 |
Marcelo Pereyra Heriot-Watt University |
Room 1 | |
15:40 to 16:00 |
Pol del Aguila Pla KTH - Royal Institute of Technology |
Room 1 | |
16:00 to 16:30 | No Room Required | ||
16:30 to 17:20 |
Claire Boyer Université Pierre et Marie Curie Paris |
Room 1 | |
17:20 to 18:10 |
Tuomo Valkonen University of Liverpool |
Room 1 | |
19:30 to 22:00 | No Room Required |
Friday 3rd November 2017 | |||
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09:00 to 09:50 |
Irene Waldspurger Université Paris-Dauphine |
Room 1 | |
09:50 to 10:40 |
Martin Holler University of Graz |
Room 1 | |
10:40 to 11:10 | No Room Required | ||
11:10 to 12:00 |
Raymond Chan Chinese University of Hong Kong |
Room 1 | |
12:00 to 12:50 |
Mihaela Pricop-jeckstadt Technische Universität Dresden |
Room 1 | |
12:50 to 14:00 | No Room Required | ||
14:00 to 14:50 |
Robert Plemmons Wake Forest University |
Room 1 | |
14:50 to 15:40 |
Jeff Calder University of Minnesota |
Room 1 | |
15:40 to 16:10 | No Room Required |
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