首页> 外文期刊>Journal of electronic imaging >Single-image superresolution based on local regression and nonlocal self-similarity
【24h】

Single-image superresolution based on local regression and nonlocal self-similarity

机译:基于局部回归和非局部自相似的单图像超分辨率

获取原文
获取原文并翻译 | 示例
       

摘要

The challenge of learning-based superresolution (SR) is to predict the relationships between low-resolution (LR) patches and their corresponding high-resolution (HR) patches. By learning such relationships from external training images, the existing learning-based SR approaches are often affected by the relevance between the training data and the LR input image. Therefore, we propose a single-image SR method that learns the LR-HR relations from the given LR image itself instead of any external images. Both the local regression model and nonlocal patch redundancy are exploited in the proposed method. The local regression model is employed to derive the mapping functions between self-LR-HR example patches, and the nonlocal self-similarity gives rise to a high-order derivative estimation of the derived mapping function. Moreover, to fully exploit the multiscale similarities inside the LR input image, we accumulate the previous reconstruction results and their corresponding LR versions as additional example patches for the subsequent estimation process, and adopt a gradual magnification scheme to achieve the desired zooming size step by step. Extensive experiments on benchmark images have validated the effectiveness of the proposed method. Compared to other state-of-the-art SR approaches, the proposed method provides photorealistic HR images with sharp edges.
机译:基于学习的超分辨率(SR)的挑战是预测低分辨率(LR)补丁与其对应的高分辨率(HR)补丁之间的关系。通过从外部训练图像中学习这种关系,现有的基于学习的SR方法通常会受到训练数据与LR输入图像之间的相关性的影响。因此,我们提出了一种单图像SR方法,该方法从给定的LR图像本身而不是任何外部图像中学习LR-HR关系。该方法利用了局部回归模型和非局部补丁冗余。采用局部回归模型来推导自LR-HR样本块之间的映射函数,并且非局部自相似性导致对推导的映射函数进行高阶导数估计。此外,为了充分利用LR输入图像内的多尺度相似性,我们将先前的重建结果及其对应的LR版本作为后续估计过程的附加示例补丁进行累积,并采用渐进式放大方案逐步实现所需的缩放大小。在基准图像上的大量实验已经验证了该方法的有效性。与其他最新的SR方法相比,该方法可提供具有清晰边缘的逼真的HR图像。

著录项

  • 来源
    《Journal of electronic imaging》 |2014年第3期|033014.1-033014.14|共14页
  • 作者

    Jing Hu; Yupin Luo;

  • 作者单位

    Tsinghua University, Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing 100084, China;

    Tsinghua University, Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing 100084, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    superresolution; self learning based; multiscale self-similarity; local regression;

    机译:超分辨率以自我学习为基础;多尺度自相似局部回归;
  • 入库时间 2022-08-18 01:17:26

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号