• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >Image Super-Resolution Based on Sparse Representation via Direction and Edge Dictionaries
【24h】

Image Super-Resolution Based on Sparse Representation via Direction and Edge Dictionaries

机译：基于方向和边缘字典的稀疏表示的图像超分辨率

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Sparse representation has recently attracted enormous interests in the field of image super-resolution. The sparsity-based methods usually train a pair of global dictionaries. However, only a pair of global dictionaries cannot best sparsely represent different kinds of image patches, as it neglects two most important image features: edge and direction. In this paper, we propose to train two novel pairs of Direction and Edge dictionaries for super-resolution. For single-image super-resolution, the training image patches are, respectively, divided into two clusters by two new templates representing direction and edge features. For each cluster, a pair of Direction and Edge dictionaries is learned. Sparse coding is combined with the Direction and Edge dictionaries to realize super-resolution. The above single-image super-resolution can restore the faithful high-frequency details, and the POCS is convenient for incorporating any kind of constraints or priors. Therefore, we combine the two methods to realize multiframe super-resolution. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed method. Experimental results demonstrate that our method can recover better edge structure and details.
机译：稀疏表示法最近在图像超分辨率领域引起了极大的兴趣。基于稀疏性的方法通常训练一对全局词典。但是，只有一对全局词典不能最好地稀疏地表示不同种类的图像块，因为它忽略了两个最重要的图像特征：边缘和方向。在本文中，我们建议训练两对新颖的Direction和Edge字典以实现超分辨率。对于单图像超分辨率，训练图像块被表示方向和边缘特征的两个新模板分别分为两个簇。对于每个群集，将学习一对Direction和Edge字典。稀疏编码与“方向”和“边缘”字典结合使用以实现超分辨率。上面的单图像超分辨率可以恢复忠实的高频细节，并且POCS便于合并任何种类的约束或先验条件。因此，我们结合两种方法来实现多帧超分辨率。进行了图像超分辨率的广泛实验，以验证该方法的通用性，有效性和鲁棒性。实验结果表明，我们的方法可以恢复更好的边缘结构和细节。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2017年第6期|3259357.1-3259357.11|共11页
  • 作者

    Zhu Xuan; Wang Xianxian; Wang Jun; Jin Peng; Liu Li; Mei Dongfeng;

  • 作者单位

    Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China;

    Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China;

    Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China;

    Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China;

    Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China;

    Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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