| 基于改进相容函数的超分辨率图像重建 |
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| 引用本文： | 张桦. 基于改进相容函数的超分辨率图像重建[J]. 光电子．激光, 2010, 0(1): 120-123 |
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| 作者姓名： | 张桦 |
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| 作者单位： | 天津理工大学计算机视觉与系统省部共建教育部重点实验室;天津市智能计算及软件新技术重点实验室; |
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| 基金项目： | 国家自然科学基金资助项目(60872064);;天津市自然科学基金资助项目(08JCYBJC12200,08JCYBJC12300) |
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| 摘 要： | 提出了一种基于机器学习的超分辨率(SR)改进算法。首先建立一个包括低分辨率(LR)图像及其相应的高分辨率(HR)图像的训练样本集,为LR图像提供了HR的图像解释。把训练集中的每一幅图像分成若干个图像块,每一个图像块作为马尔可夫随机场(MRF)模型的结点,MRF模型参数从这些训练样本中学习得到,通过对训练样本中的LR图像块进行k-均值聚类减少计算开销,并用k-均值的聚类结果提出了一种新的相容函数形式。实验结果表明,该算法是可行的,并与同类算法相比能取得较好的结果,使得SR后的图像更平滑自然。
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| 关 键 词： | 超分辨率(SR) 马尔可夫随机场(MRF) 相容函数 k-均值 |
Super-resolution image reconstruction based on improved compatibility function |
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| ZHANG Hua. Super-resolution image reconstruction based on improved compatibility function[J]. Journal of Optoelectronics·laser, 2010, 0(1): 120-123 |
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| Authors: | ZHANG Hua |
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| Affiliation: | ZHANG Hua,MA Yan-jie,XUE Yan-bing(Key Laboratory of Computer Vision , System MEC,Tianjin Key Laboratory of Intelligence Computing , Novel Software Technology,Tianjin University of Technology,Tianjin 300191,China |
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| Abstract: | An improved algorithm is presented for super resolution based on machine learning.A training sample set is set up which contains low-resolution images and the corresponding high-resolution images.These training samples provide high-resolution image interpretation for the low-resolution images.Every image in the training set is divided into several patches and each of them is assigned one node of a Markov Random Field(MRF).The parameters of MRF are learned from these training samples and the probability dist... |
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| Keywords: | super-resolution(SR) Markov random field(MRF) compatibility function k-means |
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