The learning-based methods for single image super-resolution employ an instance training database to produce a high-resolution (HR) image from a single low-resolution (LR) input. In this study

we propose a new super-resolution framework without external training database. The proposed method is based on the self-similarity of images

which is a recurrence of image patches within an image or across image scales

and the support vector regression (SVR) model derives good fitting data via nonlinear mapping. First

the image pyramid of the input LR images is established

and the set of LR/HR image patch pairs is set. Second

we search similar image patches of input LR image patch in the set of LR/HR image patch pairs. Then

we use SVR to learn the map relationship between these similarity LR image patches and the pixel value of the center of the corresponding HR images. Finally

we can obtain the HR image patch through the aforementioned relationship and input LR image patch. We tested the proposed method on seven HR images with different textures and structures

which are downsampled by Gaussian blurring under a scalar factor of 2. The average PSNRs of our method are 2.37

0.70

and 0.57 dB higher than the bicubic interpolation

the sparse representation-based super-resolution method

and the support vector regression-based super-resolution method

respectively. Experimental results show that the proposed method can effectively achieve image super-resolution reconstruction

particularly for the image with a highly similar texture.