Recovery algorithms play a key role in compressive sampling (CS).Most of current CS recovery algo-rithms are originally designed for one-dimensional (1D) signal,while many practical signals are two-dimensional (2D).By utilizing 2D separable sampling,2D signal recovery problem can be converted into 1D signal recovery problem so that ordinary 1D recovery algorithms,e.g.orthogonal matching pursuit (OMP),can be applied directly.However,even with 2D separable sampling,the memory usage and complexity at the decoder are still high.This paper develops a novel recovery algorithm called 2D-OMP,which is an extension of 1D-OMP.In the 2D-OMP,each atom in the dictionary is a matrix.At each iteration,the decoder projects the sample matrix onto 2D atoms to select the best matched atom,and then renews the weights for all the already selected atoms via the least squares.We show that 2D-OMP is in fact equivalent to 1D-OMP,but it reduces recovery complexity and memory usage significantly.What’s more important,by utilizing the same methodology used in this paper,one can even obtain higher dimensional OMP (say 3D-OMP,etc.) with ease. Recovery algorithms play a key role in compressive sampling (CS) .Most of current CS recovery algo-rithms are originally designed for one-dimensional (1D) signal while many practical signals are two-dimensional (2D) .By utilizing 2D separable sampling , 2D signal recovery problem can be converted into 1D signal recovery problem so that ordinary 1D recovery algorithms, egorthogonal matching pursuit (OMP), can be applied directly.However, even with 2D separable sampling, the memory usage and complexity at the decoder are still high. This paper develops a novel recovery algorithm called 2D-OMP, which is an extension of 1D-OMP. the 2D-OMP, each atom in the dictionary is a matrix. At each iteration, the decoder projects the sample matrix onto 2D atoms to select the best matched atom, and then renews the weights for all the already selected atoms via the least squares.We show that 2D-OMP is in fact equivalent to 1-OMP, but it reduces recovery complexity and memory usage significantly. What’s more impor tant, by utilizing the same methodology used in this paper, one can even get higher dimensional OMP (say 3D-OMP, etc.) with ease.