Abstract: The traditional dictionary learning algorithm doesn't take the internal and external characteristics of the image into account in pre-process. A novel algorithm about image entropy was proposed based on gray-gradient co-occurrence matrix solution. Calculating the entropy of image patches by gray-gradient co-occurrence matrix, each image patch was classified and each type of patches was combined into training set. All kinds of sub-dictionaries were updated by singular value decomposition algorithm based on coefficient matrix. The reconstruction experiment was carried out based on the sparse representation coefficient of the test image. The simulation results show that the algorithm can effectively produce adapted sparse dictionary and significantly improve the accuracy of the reconstruction of image.