Abstract:

Constructing an appropriate overcomplete dictionary is one of the key problems of superresolution based on sparse representation. In the maximum likelihood estimation principle, an isomorphic overcomplete dictionary learning model based on mixture Gaussian is proposed. Firstly, the sparse coding residual of the model is described by the weight l2norm and the weight matrix is designed by the residual. Secondly,the isomorphic coupled dictionary learning problem is translated into the single dictionary learning problem. The dictionary is learned by the alternate and iterative strategy using sparse coding and dictionary updating. An interiorpoint method is used in sparse coding and Lagrange dual is used in dictionary updating. Finally, the learned dictionary is used in the superresolution experiment,and compared with other methods.The experimental results demonstrate the effectiveness of the proposed method.

Key words: super-resolution;overcomplete dictionary;mixture Gaussian;sparse coding