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