CS: Block-Sparsity: Coherence and Efficient Recovery, some talks, Bayesian Optimization of MRI Sequences

Here is a new entry in arxiv entitled Block-Sparsity: Coherence and Efficient Recovery by Yonina Eldar and Helmut Bolcskei. The abstract reads:

We consider compressed sensing of block-sparse signals, i.e., sparse signals that have nonzero coefficients occuring in clusters. Based on an uncertainty relation for block-sparse signals, we define a block-coherence measure and we show that a block-version of the orthogonal matching pursuit algorithm recovers block k-sparse signals in no more than k steps if the block-coherence is sufficiently small. The same condition on block-sparsity is shown to guarantee successful recovery through a mixed l2/l1 optimization approach. The significance of the results lies in the fact that making explicit use of block-sparsity can yield better reconstruction properties than treating the signal as being sparse in the conventional sense thereby ignoring the additional structure in the problem.

I'll add the Block OMP (BOMP) algorithm to the Big Picture Reconstruction section. 

There is a seminar series at Vanderbilt on Compressed Sensing. I just noticed it but here are the last talks:

5. The Tiling Phenomenon of 1-bit Feedback Analog-to-Digital Converters

Truong-Thao Nguyen, City University of New York

Tuesday, December 2, 4:10-5:00, SC 1312

6. Compressive Signal Processing using Manifold Models

Mike Wakin, Colorado School of Mines

Tuesday, December 9, 4:10-5:00, SC 1312.

At Drexel, on January 12, 2009, Leo Grady will probably give a similar talk to the one we mentioned earlier. In the meantime,

Matthias Seeger has a new talk entitled Bayesian Optimization of MRI Sequences.

He mentions that "compared to earlier talks, this has a few more details about the inference

algorithm."

He still is looking for folks to work with him. It looks like most of the MRI trajectories in the Fourier space are in following a spiral and I wonder how the results of Meyer and Basarab would fit in this area of investigation ( I wondered this same point as while back). Maybe in the form of priors....