In order to achieve higher resolution images, a semi-coupled sparse dictionary learning algorithm by using adaptive image blocks clustering algorithm is proposed. The theoretical basis of semi-coupled sparse dictionary learning algorithm and adaptive image blocks clustering algorithm are studied in this paper. Firstly，according to the application of sparse representation theory in image super resolution algorithm，the coupled and semi-coupled sparse dictionary learning algorithm are introduced. The coupled dictionary learning algorithm assumes that sparse codings of corresponding high and low resolution image blocks are equal, but this assumption is too strict. The semi-coupled dictionary learning algorithm relaxes this assumption，assuming that sparse codings of corresponding high and low resolution image blocks are not equal，but satisfying a linear mapping. Secondly，because the semi-coupled sparse dictionary learning algorithm is more reasonable than the coupled sparse dictionary learning algorithm, in this paper，the semi-coupled sparse dictionary learning algorithm is adopted. Then, according to the limitations of expression of the global dictionary, the multi-dictionary learning algorithm is analyzed. Finally，through the analysis of the traditional image blocks clustering algorithm, a semi-coupled sparse dictionary learning algorithm by using adaptive image blocks clustering algorithm is proposed. The experimental results show that the Peak Signal to Noise Ratios(PSNR) of Butterfly, Cameraman, Foreman, Plants, Hat and Lena images obtained by the proposed algorithm are higher than that of the semi-coupled sparse dictionary learning algorithm based on K-means clustering algorithm by 0.18 dB,0.16 dB,0.52 dB,0.21 dB,0.23 dB and 0.14 dB respectively. According to the results，a conclusion is drawn that better reconstruction images can be obtained by the proposed method.