Sparse representation of signals is an evolving field in many machine learning and image processing tasks. Nowadays

more and more attention is paid on the algorithm for learning dictionaries.Traditionally

the dictionary is an unstructured set of atoms. Considering the sparsity of the group of the sparse representation signal

a mathematical model of the dictionary learning based on the group sparsity is constructed. We propose an efficient algorithm for learning structured dictionary according to the convex analysis and monotone operator theory. The experiments show that the algorithm converges faster

the dictionary trained from the new model adapts better to the data and the data is better represented

which overall improves the image enhancement effect.