Compressive Sensing Recovery Algorithms and Applications- A Survey

Compressive sensing is an efficient method of acquiring signals or images with minimum number of samples, assuming that the signal is sparse in a certain transform domain. Conventional technique for signal acquisition follows the Shannon's sampling theorem, which requires signals to be sampled at a rate atleast twice the maximum frequency (i.e f s ≥ 2 f m ). As compared with this traditional acquisition technique, compressive sensing technique captures wide range of signals at a rate significantly lower than Nyquist rate without losing the imperative information, so this technique can be widely used in MRI. Suitable reconstruction algorithms are needed for recovering the original signals from compressed sampled signal. This paper introduces a survey of the various reconstruction algorithms which might enable the use of this technology for wide spread hardware combatiable implementation in the near future.