Most methods of super resolution so far enhance images by adding exterior information extracted from a given training set. However, this is impractical in lots of cases. From analysis of an ideal edge model and texture contents within images, it is found that many images hold similar local structure at different resolution and preserve it stably in the scale space. Based on this property, Image Analogies can be applied to pass local information onto lower resolution image and thus to achieve resolution enhancement. Original image and its lower-resolution version are used to construct the training set to fit this problem to Image Analogies, and it is resolved by minimizing a graph with energy. Experimental results show that this self analogies algorithm can amplify images much more sharply than traditional interpolation-like methods, and more importantly, it can be executed independently without any supposed outliers.