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一种基于自样本学习的人脸图像超分辨率复原算法 被引量:2

An algorithm for super-resolution restoration of human faces based on self-example learning
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摘要 针对人脸图像超分辨率复原问题,提出了一种新的基于自样本学习的超分辨率复原算法。该算法从输入图像本身提取训练样本库,并采用矢量量化的方法对训练样本进行分类。再利用并行设计的多类预测器对每类样本进行学习训练,指导高频信息的估计重建。对来自输入图像本身的自样本训练集合和来自特定训练图像库的特定训练样本集合进行了对比研究。实验结果表明提出算法在图像重建质量和实现速度上都有很好的表现。 The paper proposes a novel super-resolution reconstruction algorithm for human faces. The algorithm extracts training examples from the input image and divides them into several classes using vector quantization. Then, it classifies each patch from a low-resolution image as one of these classes. Each class hag its high-frequency information inferred using a parallel designed multi-class predictor, which is trained using the training samples from the same class. The self-example training set and the specific domain training set were employed in investigation of the impact of the training database. The experimental results showed the superior performance of the proposed method in terms of both the reconstruction quality and runtime.
出处 《高技术通讯》 EI CAS CSCD 北大核心 2009年第4期377-381,共5页 Chinese High Technology Letters
基金 国家自然科学基金(60431020 60772069) 北京市自然科学基金(4062006) Research Grants Council of the Hong Kong Special Administrative Region China(PolyU 5199/06E)资助项目
关键词 超分辨率复原 人脸图像放大 示例学习 自样本 多类预测器 super-resolution restoration, human face magnification, example-based learning, self-example, multi-class predictor
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参考文献18

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二级参考文献31

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