Super-resolution of images based on local correlations
Super-resolution of images based on local correlations
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DOI：
10.1109/72.750566
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发表时间：
1999-03-01
影响因子：
--
通讯作者：
Principe, JC
中科院分区：
文献类型：
--
作者：
Candocia, FM;Principe, JC
本文提出了一种自适应的两步光学图像超分辨方法。该过程将图像样本局部投影到从图像数据学习的一系列内核上。首先，对来自训练图像的局部邻域信息执行无监督特征提取。然后，这些功能被用来聚类的邻域成不相交的集合，其中一个最佳的映射相关的同源邻域跨尺度可以学习在监督的方式，超分辨率图像是通过卷积的低分辨率测试图像与已建立的家庭的内核。实验结果证明了该方法的有效性。
An adaptive two-step paradigm for the superresolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image data. First, an unsupervised feature extraction Is performed on local neighborhood information from a training image. These features are then used to cluster the neighborhoods into disjoint sets for which an optimal mapping relating homologous neighborhoods across scales can be learned in a supervised manner, A super-resolved image is obtained through the convolution of a low-resolution test image with the established family of kernels. Results demonstrate the effectiveness of the approach.