We consider the problem of multiple correlated sparse signals reconstruction and propose a new implementation of struc- tured sparsity through a reweighting scheme. We present a particular application for diffusion Magnetic Resonance Imaging data and show how this procedure can be used for fi- bre orientation reconstruction in the white matter of the brain. In that framework, our structured sparsity prior can be used to exploit the fundamental coherence between fibre directions in neighbour voxels. Our method approaches the l0 minimi- sation through a reweighted l1-minimisation scheme. The weights are here defined in such a way to promote correlated sparsity between neighbour signals.
|Number of pages||5|
|Publication status||Published - 2015|
|Event||23rd European Signal Processing Conference 2015 - Nice, France|
Duration: 31 Aug 2015 → 4 Sep 2015
|Conference||23rd European Signal Processing Conference 2015|
|Abbreviated title||EUSIPCO 2015|
|Period||31/08/15 → 4/09/15|