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Maximizing Nonlocal Self-Similarity Prior for Single Image Super-Resolution

机译：在单图像超分辨率之前最大化非识别自相似性

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摘要

Prior knowledge plays an important role in the process of image super-resolution reconstruction, which can constrain the solution space efficiently. In this paper, we utilized the fact that clear image exhibits stronger self-similarity property than other degradated version to present a new prior called maximizing nonlocal self-similarity for single image super-resolution. For describing the prior with mathematical language, a joint Gaussian mixture model was trained with LR and HR patch pairs extracted from the input LR image and its lower scale, and the prior can be described as a specific Gaussian distribution by derivation. In our algorithm, a large scale of sophisticated training and time-consuming nearest neighbor searching is not necessary, and the cost function of this algorithm shows closed form solution. The experiments conducted on BSD500 and other popular images demonstrate that the proposed method outperforms traditional methods and is competitive with the current state-of-the-art algorithms in terms of both quantitative metrics and visual quality.
机译：先验知识在图像超分辨率重建过程中起重要作用，这可以有效地限制解决方案空间。在本文中，我们利用了清晰的图像表现出比其他退化版本更强的自相似性，以呈现用于单图像超分辨率的新的先前称为最大化非识别自相似性的新的自我相似性。为了描述现有数学语言，通过从输入LR图像提取的LR和HR贴片对训练联合高斯混合模型，并且通过推导，可以将先验描述为特定的高斯分布。在我们的算法中，不需要大规模的复杂训练和耗时耗时的邻居搜索，并且该算法的成本函数显示了封闭的形式解决方案。在BSD500和其他流行图像上进行的实验表明，所提出的方法优于传统方法，并且在定量度量和视觉质量方面具有目前最先进的算法。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第5期|3840285.1-3840285.14|共14页
  • 作者

    Li Jianhong; Wattanachote Kanoksak; Wu Yarong;

  • 作者单位

    Guangdong Univ Foreign Studies Lab Language Engn & Comp Guangzhou 510006 Guangdong Peoples R China;

    Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China;

    Zhongkai Univ Agr & Engn Zhongkai Sci & Technol Dev Co Guangzhou 510225 Guangdong Peoples R China;

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