Fast Image Super-Resolution Via Multiple Directional Transforms

Recently, single image super-resolution (SISR) is very important research field to reconstruct a high-resolution (HR) image from a low resolution (LR) image. However, existing image super-resolution approaches require a lot of computations or consider parameters for various situations. This paper proposes an efficient and simple image super-resolution technique using multiple directional lapped orthogonal transforms (M-DirLOTs). It captures high-frequency informations, e.g. edges and slant textures, of images efficiently, and reduce the computational cost. Simultaneously, this model avoids any a priori hypotheses on the LR picture. The proposed method overcomes some disadvantages of existing methods. Experimental results show that the proposed method is able to significantly improve the super resolution performance.