...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on multimedia >Self-Learning Super-Resolution Using Convolutional Principal Component Analysis and Random Matching
【24h】

Self-Learning Super-Resolution Using Convolutional Principal Component Analysis and Random Matching

机译：使用卷积主成分分析和随机匹配的自学习超分辨率

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Self-learning super-resolution (SLSR) algorithms have the advantage of being independent of an external training database. This paper proposes an SLSR algorithm that uses convolutional principal component analysis (CPCA) and random matching. The technologies of CPCA and random matching greatly improve the efficiency of self-learning. There are two main steps in this algorithm: forming the training and testing the data sets and patch matching. In the data set forming step, we propose the CPCA to extract the low-dimensional features of the data set. The CPCA uses a convolutional method to quickly extract the principal component analysis (PCA) features of each image patch in every training and testing image. In the patch matching step, we propose a two-step random oscillation accompanied with propagation to accelerate the matching process. This patch matching method avoids exhaustive searching by utilizing the local similarity prior of natural images. The two-step random oscillation first performs a coarse patch matching using the variance feature and then performs a detailed matching using the PCA feature, which is useful to find reliable matching patches. The propagation strategy enables patches to propagate the good matching patches to their neighbors. The experimental results demonstrate that the proposed algorithm has a substantially lower time cost than that of many existing self-learning algorithms, leading to better reconstruction quality.
机译：自学习超分辨率（SLSR）算法具有独立于外部培训数据库的优势。本文提出一种使用卷积主成分分析（CPCA）和随机匹配的SLSR算法。 CPCA技术和随机匹配技术极大地提高了自学效率。该算法有两个主要步骤：形成训练和测试数据集以及补丁匹配。在数据集形成步骤中，我们建议CPCA提取数据集的低维特征。 CPCA使用卷积方法快速提取每个训练和测试图像中每个图像补丁的主成分分析（PCA）特征。在补丁匹配步骤中，我们提出了一个两步随机振荡并伴随传播，以加快匹配过程。该补丁匹配方法通过利用自然图像的局部相似度来避免穷举搜索。两步随机振荡首先使用方差特征执行粗略的斑块匹配，然后使用PCA特征进行详细的匹配，这对于找到可靠的匹配斑块很有用。传播策略使补丁能够将匹配良好的补丁传播到其邻居。实验结果表明，与许多现有的自学习算法相比，该算法的时间成本大大降低，重构质量更高。

著录项

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