title: Single Image Super-Resolution Using Local  Geometric Duality and Non-Local Similarity.
abstract: Super-resolution (SR) from a single image plays an important role in many computer vision applications. It aims to estimate a high-resolution (HR) image from an input low- resolution (LR) image. To ensure a reliable and robust estimation of the HR image, we propose a novel single image SR method that exploits both the local geometric duality (GD) and the non-local similarity of images. The main principle is to formulate these two typically existing features of images as effective priors to constrain the super-resolved results. In consideration of this principle, the robust soft-decision interpolation method is generalized as an outstanding adaptive GD (AGD)-based local prior. To adaptively design weights for the AGD prior, a local non-smoothness detection method and a directional standard-deviation-based weights selection method are proposed. After that, the AGD prior is combined with a variational-framework-based non-local prior. Furthermore, the proposed algorithm is speeded up by a fast GD matrices construction method, which primarily relies on the selective pixel processing. The extensive experimental results verify the effectiveness of the proposed method compared with several state-of-the-art SR algorithms.
authors.forename: Chao
authors.initials: C
authors.lastname: Ren
authors.forename: Xiaohai
authors.initials: X
authors.lastname: He
authors.forename: Qizhi
authors.initials: Q
authors.lastname: Teng
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Wu
authors.forename: Truong Q
authors.initials: TQ
authors.lastname: Nguyen