In the big data era, in order to control the cost, complexity, and bandwidth of collecting and processing high-dimensional data systems, it is critical to exploit models that encapsulate prior information regarding the signals of interest The compresive sensing is a new signal processing technique which may use it instead of nyquist sampling theoreme, it's about sensing and compressing in the same time.
Compresed sensing has many applications indcluding MRI, RADARS,..this new technique rely on sparsity of the signal of interest.
In addition of sensing and compressing signal we need to reconstrut the signal when we would like to apply some process.., for this reason there are several algorithms that can be classified into 3 types: *Optimization Methodes *Greedy Methods *Thresholding-Based Methods Till here it's okay
my questions are : What is(are) the most efficient recovery algorithms ? What are the metrics used to define performance of a recovery algorithm ?