Compressed Sensing: SPARCO is your friend.

In October, I mentioned the birth of SPARCO ( by Ewout van den Berg, Michael P. Friedlander, Gilles Hennenfent, Felix J. Herrmann, Rayan Saab, and Ozgur Yilmaz) a new framework where one could try his/her reconstruction technique and follow the reproducible research guidelines. The software has grown to be a little bit more since then it seems and now it provides a good platform for learning about Compressed Sensing or Compressive Sensing by running different cases and have direct access to the sometime strange measurement matrices found in the literature. Not unlike Wavelab, SPARCO provides a set of problem example that have been worked out and published. All these problem sets are nicely featured in this page. Not only does it list the problem but it also provides the reader with the Measurement matrix type and the Sparsity matrix as well. This is is a nice touch.

Of further interest is the ability to use complex operations that enable to build measurement or sparsity operators as well as meta-operators. A lot has gone into this and the authors should be thanked for it.


The technical report provides ample information on how these examples and operators work. In the documentation, one can see how to rapidly prototype a new case with SPARCO operators. I have had some problems running some of the examples because of functions of the Rice Wavelet Toolbox not getting the right parameters.

Besides this minor problem, SPARCO seems a solid platform that will grow by including other dictionaries, measurement matrices and reconstruction operators to the satisfaction of the whole community. Eventually, we'll need operators to bring the dictionaries found from tensor factorizations into 2-D that SPARCO can handle.