Adaptive Single Image Superresolution Approach Using Support Vector Data Description
Adaptive Single Image Superresolution Approach Using Support Vector Data Description
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DOI：
10.1155/2011/852934
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发表时间：
2011-03
影响因子：
1.9
通讯作者：
Takahiro Ogawa;M. Haseyama
中科院分区：
文献类型：
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作者：
Takahiro Ogawa;M. Haseyama
提出了一种基于支持向量数据描述（SVDD）的自适应单图像超分辨率（SR）方法。所提出的方法表示先验的高分辨率（HR）图像的超球体的SVDD从训练的例子和重建HR图像从低分辨率（LR）的观察的基础上，以下计划。首先，为了执行包含各种对象的HR图像的精确重构，基于距针对每个聚类获得的超球体的中心的距离来预先对训练HR示例进行聚类。此外，估计目标图像的缺失高频分量，以便重建的HR图像最小化上述距离。在这种方法中，最小化的距离获得的每个集群被用作一个标准，以选择最佳的超球估计的高频分量。该方法提供了对常规方法不能执行高频分量的自适应估计的问题的解决方案。此外，目标低分辨率（LR）图像中的局部补丁被用作训练HR的例子，从一般图像的不同分辨率之间的自相似性的特点，我们的方法可以执行SR，而不利用任何其他HR图像。
An adaptive single image superresolution (SR) method using a support vector data description (SVDD) is presented. The proposed method represents the prior on high-resolution (HR) images by hyperspheres of the SVDD obtained from training examples and reconstructs HR images from low-resolution (LR) observations based on the following schemes. First, in order to perform accurate reconstruction of HR images containing various kinds of objects, training HR examples are previously clustered based on the distance from a center of a hypersphere obtained for each cluster. Furthermore, missing high-frequency components of the target image are estimated in order that the reconstructed HR image minimizes the above distances. In this approach, the minimized distance obtained for each cluster is utilized as a criterion to select the optimal hypersphere for estimating the high-frequency components. This approach provides a solution to the problem of conventional methods not being able to perform adaptive estimation of the high-frequency components. In addition, local patches in the target low-resolution (LR) image are utilized as the training HR examples from the characteristic of self-similarities between different resolution levels in general images, and our method can perform the SR without utilizing any other HR images.