  The Compressive Sensing (CS) as a novel acquisition approach that finds its
usage in image processing. The hypothesis like this one assures signal recovery
with high quality from decreased number of samples compared with the number
required by the Nyquist - Shannon sampling theorem. It includes a gathering of
strategies for representing a signal that are based on the predetermined number
of estimations and after that signal reconstruction. The CS has been broadly
utilized and applied in numerous applications including computed tomography,
WiFi communication, image processing and camera design. Complex mathematics is
developed in order to ensure signal reconstruction from relatively small
information. Two commonly used groups of the algorithms are convex optimization
and greedy approaches.
