TY  - JOUR
AB  - 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. © 2011 IEEE.
AU  - Gao, X
AU  - Zhang, K
AU  - Tao, D
AU  - Li, X
DA  - 2012/02/01
DO  - 10.1109/TIP.2011.2161482
EP  - 480
JO  - IEEE Transactions on Image Processing
PY  - 2012/02/01
SP  - 469
TI  - Joint learning for single-image super-resolution via a coupled constraint
VL  - 21
Y1  - 2012/02/01
Y2  - 2026/07/04
ER  -