Abstract
Position-patch based approaches have been proposed for single-image face hallucination. This paper models the face hallucination problem as a coefficient recovery problem with respect to an adaptive training set for improved noise robustness. The image-adaptive training set is constructed by corrupting a local training set of position-patches by adding specific amounts of noise depending on the input image noise level. In this proposed method, image denoising and super-resolution are simultaneously carried out to obtain superior results. Though the principle is general and can be extended to most super-resolution algorithms, we discuss this in context of existing locality-constrained representation (LcR) approach in order to compare their performances. It can be demonstrated that the proposed approach can quantitatively and qualitatively yield better results in high noisy environments.
Published Version
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