Today I spent some time in trying to understand how Compress Sensing works.

I got through the idea of using the sparsity of the transform domain and reducing the degrees of freedom and arrive at this sparse elements with a relatively small set of sampled points.

Some one said that nyquist was a pessimist as he gave the upper bound. Now we are interested in the lower bound …..

A decently good tutorial (Though I didnt understand the head or tail of UUP or RIP … Need to spend more time)

http://www.ee.duke.edu/ssp07/Tutorials/ssp07-cs-tutorial.pdf

This blog seemed to cover lot on CS …. I spent some time on the links provided …

http://igorcarron.googlepages.com/cs

At present I m reading this article called sparseMRI , where cs is used to get the Scan Time optimization on the cartesian Grid for angiogram images … sparsemri1

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