Concerning the slow reconstruction and the difference among the contents of the image to be reconstructed, an improved super-resolution image reconstruction algorithm based on image patche iteration and sparse representation was proposed. In the proposed method, image patches were firstly divided into three different forms by threshold features, then the three forms were treated separately: during the reconstruction process, Bicubic Interpolation (BI) approach was used for image patches of 4 N×4 N; image patches of 2 N×2 N achieved corresponding high and low resolution dictionary pairs by K-Singular Value Decomposition (K-SVD) algorithm, and then to finish reconstruction using Orthogonal Matching Pursuit (OMP) algorithm; image patches of N× N were divided into smoothing layer and texture layer by Morphological Component Analysis (MCA) algorithm, then to finish reconstruction using OMP with corresponding dictionary pairs of each layer. Compared with the methods based on sparse representation group, MCA, and two-stage multi-frequency-band dictionaries, the proposed algorithm has a significant improvement in subjective visual effect, evaluation index and reconstruction speed. The experimental results show that the proposed algorithm can obtain more details in edge patches and irregular structure regions with better reconstruction effect.