Joint Learning for Single-Image Super-Resolution via a Coupled Constraint

  • Authors:

  • Xinbo Gao;Kaibing Zhang;Dacheng Tao;Xuelong Li

  • Affiliations:

  • School of Electronic Engineering, Xidian University, Xi'an, China;School of Electronic Engineering, Xidian University, Xi'an, China;Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia;Center for Optical Imagery Analysis and Learning, State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences, Xi'an, Chin ...

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2012

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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.