This paper presents a new method of producing a high-resolution image from a single low-resolution image without any external training image sets. We use a dictionary-based regression model for practical image super-resolution using local self-similar example patches within the image. Our method is inspired by the observation that image patches can be well represented as a sparse linear combination of elements from a chosen over-complete dictionary and that a patch in the high-resolution image have good matches around its corresponding location in the low-resolution image. A first-order approximation of a nonlinear mapping function, learned using the local self-similar example patches, is applied to the low-resolution image patches to obtain the corresponding high-resolution image patches. We show that the proposed algorithm provides improved accuracy compared to the existing single image super-resolution methods by running them on various input images that contain diverse textures, and that are contaminated by noise or other artifacts.