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首页> 外文期刊>Signal processing >Noise robust face hallucination algorithm using local content prior based error shrunk nearest neighbors representation
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Noise robust face hallucination algorithm using local content prior based error shrunk nearest neighbors representation

机译：基于局部内容基于先验的误差缩小的最近邻居表示的鲁棒性面部幻觉算法

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摘要

In recent years face hallucination or super-resolution (SR) is getting much attention due to its wide applicability in real world scenarios. The existing SR methods and models perform well for noise free or small camera/atmospheric noisy faces. However, when suffering from mixed Impulse-Gaussian (MIG) noise, face hallucination becomes a challenging task. To address this problem, a novel error shrunk nearest neighbors representation (ESNNR) based face hallucination algorithm is proposed in this paper. Here, local content prior is incorporated to identify the high variance content (HVC) in the input images. The proposed algorithm suppresses the identified HVC in the input face to minimize the squared error. Moreover, the similarity matching between the input and training images is improved to achieve the locality and sparsity in the presence of MIG noise. Simulation results performed on public FEI, CAS-PEAL, CMU+MIT face databases, and locally captured surveillance video frames show that the proposed algorithm is computationally efficient, suitable for practical applications and give better performance than the existing face SR methods.
机译：近年来，由于幻觉或超分辨率（SR）在现实世界场景中的广泛应用，备受关注。现有的SR方法和模型对于无噪点或较小的相机/大气噪点的脸部效果很好。但是，当遭受混合脉冲-高斯（MIG）噪声时，幻觉成为一项艰巨的任务。为了解决这个问题，本文提出了一种基于误差缩小的最近邻表示（ESNNR）的幻觉算法。在此，结合了本地内容优先级以识别输入图像中的高方差内容（HVC）。所提出的算法抑制了在输入面中识别出的HVC，以使平方误差最小。此外，改善了输入图像和训练图像之间的相似度匹配，以在存在MIG噪声的情况下实现局部性和稀疏性。在公共FEI，CAS-PEAL，CMU + MIT人脸数据库和本地捕获的监视视频帧上进行的仿真结果表明，该算法具有较高的计算效率，适合实际应用，并且比现有的人脸SR方法具有更好的性能。

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  • 来源
    《Signal processing》 |2018年第6期|233-246|共14页
  • 作者

    Shyam Singh Rajput; Ankur Singh; K.V. Arya; Junjun Jiang;

  • 作者单位

    Multimedia and Information Security Research Group, ABV-Indian Institute of Information Technology and Management;

    Multimedia and Information Security Research Group, ABV-Indian Institute of Information Technology and Management;

    Multimedia and Information Security Research Group, ABV-Indian Institute of Information Technology and Management,Department of Computer Science & Engineering, Institute of Engineering and Technology;

    School of Computer Science, China University of Geosciences;

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  • 正文语种 eng
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  • 关键词

    Super-resolution; Face hallucination; Learning and position-patch based method;

    机译：超分辨率;幻觉;基于学习和位置补丁的方法;

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