Image analogies method based on learned dictionary
基于学习字典的图像类推方法*

To improve the computational efficiency of image analogies,this paper presented a novel image analogies method based on learned dictionary . The method first segments sample image pairs to patches, which were unified for sparse coding and training learned dictionary. The sparse association between the patch pairs was then built, and defined as a priori knowledge for image analogies. The method mainly included two processes: training learned dictionary and image analogies. The dictionary training process could be off-line achieved to improve the computation speed, accordingly realized numerous samples training. During image analogied process, our method used the linear optimization problem of sparse prior instead of searching and matching in general methods, and improved the computational efficiency remarkably. Experiments with texture-by-numbers, stylized filter, etc. show the high efficiency of our method.