  Compressive Sensing, as an emerging technique in signal processing is
reviewed in this paper together with its common applications. As an alternative
to the traditional signal sampling, Compressive Sensing allows a new
acquisition strategy with significantly reduced number of samples needed for
accurate signal reconstruction. The basic ideas and motivation behind this
approach are provided in the theoretical part of the paper. The commonly used
algorithms for missing data reconstruction are presented. The Compressive
Sensing applications have gained significant attention leading to an intensive
growth of signal processing possibilities. Hence, some of the existing
practical applications assuming different types of signals in real-world
scenarios are described and analyzed as well.
