title: Dual-geometric neighbor embedding for image super resolution with sparse tensor.
abstract: Neighbors embedding (NE) technology has proved its efficiency in single image super resolution (SISR). However, image patches do not strictly follow the similar structure in the low-resolution and high-resolution spaces, consequently leading to a bias to the image restoration. In this paper, considering that patches are a set of data with multiview characteristics and spatial organization, we advance a dual-geometric neighbor embedding (DGNE) approach for SISR. In DGNE, multiview features and local spatial neighbors of patches are explored to find a feature-spatial manifold embedding for images. We adopt a geometrically motivated assumption that for each patch there exists a small neighborhood in which only the patches that come from the same feature-spatial manifold, will lie approximately in a low-dimensional affine subspace formulated by sparse neighbors. In order to find the sparse neighbors, a tensor-simultaneous orthogonal matching pursuit algorithm is advanced to realize a joint sparse coding of feature-spatial image tensors. Some experiments are performed on realizing a 3X amplification of natural images, and the recovered results prove its efficiency and superiority to its counterparts.
authors.forename: Shuyuan
authors.initials: S
authors.lastname: Yang
authors.forename: Zhiyi
authors.initials: Z
authors.lastname: Wang
authors.forename: Liao
authors.initials: L
authors.lastname: Zhang
authors.forename: Min
authors.initials: M
authors.lastname: Wang