Self-learning super-resolution is an approach for enhancing single-image resolution. In this approach, instead of using the external database for learning the relation between low and high resolution image patches, only relation between patches in the input image pyramid are used for learning. In this paper, a novel self-learning single image super-resolution method by focusing on the organization of the low and the corresponding high-resolution information has been presented. In order to provide training data the low-resolution and the corresponding highresolution images are created by down-sampling and up-sampling of the input image in two image pyramids. In this paper, unlike most prior super-resolution methods, the images in the low-resolution pyramid are segmented and then used for the process of super-resolution. Another remarkable point in this paper is dividing all the images of different levels of the pyramid into the same numbers and similar regions. This is done by segmenting the image at the lowest level of the pyramid and generalizing its regions to the higher-level of the pyramid images. Due to the different number of regions in each input image, the number of training models of the proposed method is different for each image and depends on the content of the input image. The result of the experiments shows that the proposed method is quantitatively and qualitatively improved the previous methods.