  Recently sparse representation has gained great success in face image
super-resolution. The conventional sparsity-based methods enforce sparse coding
on face image patches and the representation fidelity is measured by
$\ell_{2}$-norm. Such a sparse coding model regularizes all facial patches
equally, which however ignores distinct natures of different facial patches for
image reconstruction. In this paper, we propose a new weighted-patch
super-resolution method based on AdaBoost. Specifically, in each iteration of
the AdaBoost operation, each facial patch is weighted automatically according
to the performance of the model on it, so as to highlight those patches that
are more critical for improving the reconstruction power in next step. In this
way, through the AdaBoost training procedure, we can focus more on the patches
(face regions) with richer information. Various experimental results on
standard face database show that our proposed method outperforms
state-of-the-art methods in terms of both objective metrics and visual quality.
