Compressive sampling (CS) has been an effective research
area which plays an efficient role in many applications such as
cognitive radio, imaging, radar and many other applications. In
CS only a small number of linear measurements are used for
reconstruction of the signal. The significant condition for dealing
with compressed sensing system is that the signal in the input
must be sparse. Most signals in nature are sparse or can be
transformed to sparse by using any transform domain. This
paper modifies all the recovery algorithms by using the
proposed complex to real transformation algorithm. Conversion
from not sparse signal to sparse by using Fourier transform will
produce complexity, where this complexity can be removed
using complex to real transformation algorithm and then
applying it on all recovery algorithms to enhance their
performance. By using the proposed algorithm, the sparse
signal will be recovered in minimum error and less time. Also,
the signal to error ratio from the recovery process is increased.