Single image super-resolution reconstruction using multiple dictionaries and improved iterative back-projection

In order to improve the super-resolution reconstruction effect of the single image, a novel multiple dictionaries learning via support vector regression (SVR) and improved iterative back-projection (IBP) are proposed. To characterize the image structure, the low-frequency dictionary is constructed from the normalized brightness of low-frequency image patches in a discrete-cosine-transform (DCT) domain. Pixels determined by Gaussian weighting are added to the input vector to restore more high-frequency information when training the high-frequency image patch dictionary in the space domain. During post-processing, the improved IBP is employed to reduce regression errors each time. Experiment results show that the peak signal-to-noise ratio (PSNR)and structural similarity (SSIM) of the proposed method are enhanced by 1.6%–5.5% and 1.5%–13.1% compared with those of bicubic interpolation, and the proposed method visually outperforms several algorithms.