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• W2898202588 abstract "Face hallucination refers an application-specific super-resolution (SR) which predicts high-resolution images from one or multiple low-resolution inputs. Learning-based SR algorithms infer latent HR images by the guidance of coexisted priors from training samples. Various regularization methods have been successfully applied in face hallucination to ameliorate its ill-posed nature. But most of them only consider the local manifold geometry of a single patch which results in an unstable solution for SR reconstruction. In this paper, we propose a novel face hallucination algorithm to embed group patches for accurate prior representation and reconstruction. First, we select multiple recurrent self-similar patches to form a group embedding matrix. Then, a graph regularization term and another multiple-manifold regularization term are used to exploit accurate representation for SR performance. Our resulting ADMM algorithm gives a stable solution in an iterative manner. Furthermore, we use a two-step searching strategy for accelerated patch matching. Experimental results on the LFW database, FEI database, and some real-world images demonstrate the superiority of the proposed method when compared with state-of-the-art face hallucination results both on subjective and objective qualities." @default.
• W2898202588 created "2018-11-02" @default.
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• W2898202588 date "2018-01-01" @default.
• W2898202588 modified "2023-10-17" @default.
• W2898202588 title "Group Embedding for Face Hallucination" @default.
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• W2898202588 doi "https://doi.org/10.1109/access.2018.2877422" @default.
• W2898202588 hasPublicationYear "2018" @default.
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