title: Joint learning for single-image super-resolution via a coupled constraint.
abstract: The neighbor-embedding (NE) algorithm for single-image super-resolution (SR) reconstruction assumes that the feature spaces of low-resolution (LR) and high-resolution (HR) patches are locally isometric. However, this is not true for SR because of one-to-many mappings between LR and HR patches. To overcome or at least to reduce the problem for NE-based SR reconstruction, we apply a joint learning technique to train two projection matrices simultaneously and to map the original LR and HR feature spaces onto a unified feature subspace. Subsequently, the  k -nearest neighbor selection of the input LR image patches is conducted in the unified feature subspace to estimate the reconstruction weights. To handle a large number of samples, joint learning locally exploits a coupled constraint by linking the LR-HR counterparts together with the  K-nearest grouping patch pairs. In order to refine further the initial SR estimate, we impose a global reconstruction constraint on the SR outcome based on the maximum a posteriori framework. Preliminary experiments suggest that the proposed algorithm outperforms NE-related baselines.
authors.affiliation: School of Electronic Engineering, Xidian University, Xi'an 710071, China. xbgao@mail.xidian.edu.cn
authors.forename: Xinbo
authors.initials: X
authors.lastname: Gao
authors.forename: Kaibing
authors.initials: K
authors.lastname: Zhang
authors.forename: Dacheng
authors.initials: D
authors.lastname: Tao
authors.forename: Xuelong
authors.initials: X
authors.lastname: Li