New Orhagonal Matching Pursuit Algorithm for 2D different dimenssion sparse signal reconstruction

The recovery algorithm is the backbone of compressed sensing (CS). Most of the algorithms were designed for 1D signal reconstruction and a few were designed for 2D signals. To reconstruct 2D signals, in most cases signals were converted into 1D signal. In some application signals and images with 2D different dimension (N-by-M) can´t be recovered with existing 1D or 2D recovery algorithms, thus in this work we proposed a new recovery algorithm known as 2D Different Dimension Orthogonal Matching Pursuit (OMP) Algorithm, so as to solve the problem of 2D Different Dimension Sparse Signals and images, which is represented an accomplishment of 2D OMP. In the 2D different dimension OMP the observation / measurement is processed as a matrix instead of vectors in the other CS algorithms. Comparing the proposed algorithm with current algorithms, the proposed algorithm reconstructs the 2D different dimension sparse signal faster than the other algorithms, and can be implemented easier.