Fast image super-resolution via multiple directional transforms
Zhiyu Chen, Shogo Muramatsu, Yoshito Abe · 2016
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 superresolution performance.