华书娜, 王培康, 朱高. 基于聚类和高斯过程回归的超分辨率重建[J]. 微电子学与计算机, 2012, 29(10): 26-29.
引用本文: 华书娜, 王培康, 朱高. 基于聚类和高斯过程回归的超分辨率重建[J]. 微电子学与计算机, 2012, 29(10): 26-29.
HUA Shu-na, WANG Pei-kang, ZHU Gao. Super-resolution Reconstruction Based on Clustering and Gaussian Process Regression[J]. Microelectronics & Computer, 2012, 29(10): 26-29.
Citation: HUA Shu-na, WANG Pei-kang, ZHU Gao. Super-resolution Reconstruction Based on Clustering and Gaussian Process Regression[J]. Microelectronics & Computer, 2012, 29(10): 26-29.

基于聚类和高斯过程回归的超分辨率重建

Super-resolution Reconstruction Based on Clustering and Gaussian Process Regression

  • 摘要: 在样本学习的思想框架下,针对图像超分辨率问题的研究,提出了数据聚类和高斯过程回归相结合的解决方法.使用K-means对数据进行聚类,在各类中利用高斯过程回归对样本库中高低分辨率图像之间的对应关系进行学习.根据得到的学习模型对需要处理的低分辨率图像所对应的高分辨率图像进行预测,有效地利用了高低分辨率图像之间的统计特性.实验结果表明该方法可以较好地改善超分辨率重建效果.

     

    Abstract: To solve image super-resolution problems, in a learning-based framework, clustering method and the Gaussian Process Regression are employed.The k-means algorithm is used to make data clustered.And in each cluster, the relationship between the low-resolution images and the high-resolution images is modelled by the Gaussian Process Regression through the learning in the training dataset.With the learned model, the high-resolution image can be obtained by given a input low-resolution image.The statistical relationship between the low-resolution images and the high-resolution images is effectively utilized and the results demonstrate the proposed algorithm can make improvement in super resolution reconstruction.

     

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