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首页> 外文期刊>IEEE transactions on multimedia >Self-Learning Super-Resolution Using Convolutional Principal Component Analysis and Random Matching
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Self-Learning Super-Resolution Using Convolutional Principal Component Analysis and Random Matching

机译：自学习超分辨率使用卷积主成分分析和随机匹配

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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训练数据库的优点。本文提出了一种SLSR算法，它使用卷积主成分分析（CPCA）和随机匹配。 CPCA和随机匹配的技术大大提高了自学效率。该算法中有两个主要步骤：形成培训并测试数据集和补丁匹配。在数据集形成步骤中，我们提出了CPCA来提取数据集的低维特征。 CPCA使用卷积方法在每个训练和测试图像中快速提取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，伴随着传播以加速匹配过程。这种补丁匹配方法通过利用自然图像之前的局部相似性来避免穷举搜索。两步随机振荡首先使用方差特征执行粗糙的补丁匹配，然后使用PCA功能执行详细匹配，这对于找到可靠的匹配补丁是有用的。传播策略使修补程序能够将良好的匹配补丁传播到其邻居。实验结果表明，该算法的时间成本高于许多现有的自学习算法，导致更好的重建质量。

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