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Group Embedding for Face Hallucination
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DOI：10.1109/ACCESS.2018.2877422.png)
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摘要
En 中文
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.
Keyword:
Face hallucination
group embedding
graph-regularization
manifold regularization
binary hashing representation
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Multi-Manifold Locality Graph Embedding Based on the Maximum Margin Criterion (MLGE/MMC) for Face Recognition基于最大间隔准则 (MLGE/MMC) 的多流形局部图嵌入人脸识别.png)
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