The article discusses a method for reconstructing sparse 2D signals from incomplete measurements. The researchers developed a sequential sparse recovery algorithm to reconstruct the sparse matrix by observing and reconstructing it in stages. This approach reduces the sparsity of the matrix after down-sampling and allows for effective reconstruction of the 2D sparse signals. Simulation results confirmed the effectiveness of this method in reconstructing sparse signals.