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News

PhD Student: The Institute for Mathematical Stochastics (Prof. Dr. Anja Sturm) seeks qualified applicants for the position of a PhD student starting on or after February 01, 2018 for the duration of three years (more information is available here).


Student assistant: The Institute for Mathematical Stochastics (Prof. Dr. Axel Munk) seeks a student assistant for the Research Training Group 2088 in the project A1 “Wasserstein Metrics in Statistics: Inference”, starting on or after January 1, 2018 (for more details see here).


Next talk in the Stochastics Colloquium:
25.10.2017, 11:15, Dr. Vlada Limic (Université de Strasbourg)

"What is a uniform distribution?" (Abstract).
Statistics Meets Friends: The workshop "Statistics Meets Friends - from biophysics to inverse problems and back -" takes place in Göttingen from November 29th to December 1st, 2017.
BMBF INVERS

BMBF INVERS - 03MUPAH6 - Health and Medical Technology:
Deconvolution with sparsity constraints in optical nanoscopy and mass spectroscopy

Partner:

Project Managment:

  • Prof. Dr. Thorsten Hohage
  • Prof. Dr. Axel Munk

Staff:

  • Dr. Thomas Hotz
  • Philipp Marnitz


Description:

 

Mass spectroscopy and light microscopy have been revolutionised over the last couple of years, changing the requirements of the accompanying data analytical methods. We hence aim at developing new approaches to analyse such data, making them available to our industry partners.


Subprojects:

  • Deconvolution with a-priori known sparsity (T. Hohage, A. Munk, T. Hotz):
    Nowadays, light microscopy achieves resolutions which almost allow to localise individual fluorescing molecules. Thus, the common assumption used when
    applying reconstruction methods, namely that the object is (piecewise) smooth, is no longer fulfilled. Rather, the object compises few, bright and isolated points whose location and brightness are to reconstruct. This is to say they are extremely sparse, requiring penalties adapted to this situation: we are looking for reconstructions consisting of as few points as possible, while adequately explaining the data.
  • Local choice of penalties for image reconstruction in fluorescence microscopy (A. Munk, N. Bissantz, P. Marnitz):
    Reconstruction methods typically depend on some regularisation parameter allowing to balance data fit and roughness of the reconstruction. As the image structure as well as the underlying object's structure vary through the image, it is desirable to choose the regularisation parameter locally in an adaptive manner. Since statistical multiscale analysis has been proven well-suited to choose such a parameter globally for positron emission tomography (Bissantz, Mair, Munk: 2006), we envision to use it also for the problem of choosing the parameter locally.

Further information: http://www.stochastik.math.uni-goettingen.de/invers/index.php