Polarized CMB recovery with sparse component separation

@inproceedings{Bobin2015PolarizedCR,
  title={Polarized CMB recovery with sparse component separation},
  author={J{\'e}r{\^o}me Bobin and Florent Sureau and Jean-Luc Starck},
  year={2015}
}
The polarization modes of the cosmological microwave background are an invaluable source of information for cosmology, and a unique window to probe the energy scale of inflation. Extracting such information from microwave surveys requires disentangling between foreground emissions and the cosmological signal, which boils down to solving a component separation problem. Component separation techniques have been widely studied for the recovery of CMB temperature anisotropies but quite rarely for… 
1 Citations
Learning sparse representations on the sphere
TLDR
It is shown that one can directly learn a representation system from given data on the sphere, and two new adaptive approaches are proposed: the first is a (potentially multiscale) patch-based dictionary learning approach, and the second consists in selecting a representation from among a parametrized family of representations, the α-shearlets.

References

Handbook of Blind Source Separation: Independent Component Analysis and Applications
TLDR
This handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing.