Information field theory - turning data into images
by
T. Ensslin((MPI Astrophysics, Garching))
→
Europe/Stockholm
Description
Non-linear image reconstruction and signal analysis deal with complex
inverse problems. To tackle such problems in a systematic way, I present
information field theory (IFT, [1]) as a means of probabilistic, data
based inference on spatially distributed
signal fields. IFT is a statistical field theory, which permits the
construction of optimal signal recovery algorithms even for non-linear
and non-Gaussian signal inference problems. IFT algorithms exploit
spatial correlations of the signal fields and benefit from many
techniques developed to tackle statistical field theories, such
as Feynman diagrams [1], re-normalisation calculations [1,2], and
thermodynamic potentials [3]. The theory can be used in many areas, and
a few applications in cosmography, cosmic microwave background research,
and cosmic magnetism studies are presented [4].
References:
[1] T.A. Enßlin, M. Frommert, & F.S. Kitaura, Phys. Rev. D 80, 105005
(2009).
[2] T.A. Enßlin & M. Frommert, Physical Review D 83, 105014 (2011).
[3] T.A. Enßlin & C. Weig, Physical Review E 82, 051112 (2010).
[4] see applications at http://www.mpa-garching.mpg.de/ift