Super-resolution Imaging Based on Global Interpolation and Structural Similarities

In this paper, we propose a double dictionary learning method for image super-resolution (SR) reconstruction. Different from existing dictionary learning based super-resolution, we combine both self-similarity and external images to construct a double dictionary learning method. A new optimization model is established using self-similarities and external-similarities as regularization terms. Furthermore, we propose a global interpolation method to reconstruct an accurate initial estimation at the edges. Experimental results show that the proposed algorithm can produce high-quality reconstruction results both perceptually and quantitatively in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as compared to existing algorithms.