[1]姚佳丽,李中源,吴华珍,等.基于字典学习的超分辨率显微CT图像重建[J].东南大学学报(自然科学版),2016,46(5):957-963.[doi:10.3969/j.issn.1001-0505.2016.05.010]
 Yao Jiali,Li Zhongyuan,Wu Huazhen,et al.Super-resolution image reconstruction for micro-CT based on dictionary learning[J].Journal of Southeast University (Natural Science Edition),2016,46(5):957-963.[doi:10.3969/j.issn.1001-0505.2016.05.010]
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基于字典学习的超分辨率显微CT图像重建()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
46
期数:
2016年第5期
页码:
957-963
栏目:
计算机科学与工程
出版日期:
2016-09-20

文章信息/Info

Title:
Super-resolution image reconstruction for micro-CT based on dictionary learning
作者:
姚佳丽李中源吴华珍李光罗守华
东南大学生物科学与医学工程学院, 南京 210096
Author(s):
Yao Jiali Li Zhongyuan Wu Huazhen Li Guang Luo Shouhua
School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
关键词:
超分辨率重建 字典学习 面积权值 微计算机断层扫描技术
Keywords:
super-resolution reconstruction dictionary learning area weight micro computed tomography(micro-CT)
分类号:
TP391.1
DOI:
10.3969/j.issn.1001-0505.2016.05.010
摘要:
为提高显微CT重建图像的空间分辨率,提出了一种基于字典学习的超分辨率图像重建算法.首先,将重建图像进行网格细化,并使用面积权值模型实现对投影过程的精确建模.然后,选择高质量的图像作为训练样本,采用K-SVD算法构建图像字典.基于该图像字典,利用正交匹配追踪算法实现对重建图像的稀疏表达,并以此作为稀疏项约束引入到重建算法的目标函数中.最后,使用梯度下降法求解目标函数.实验结果表明:与传统的基于插值的超分辨率重建算法相比,所提算法的超分辨率结果在图像对比度、边缘保持方面具有优势,并且保留了更多的图像高频信息,从而有效提高了重建图像的空间分辨率.
Abstract:
To improve the spatial resolution of reconstructed images for micro computed tomography(micro-CT), a super-resolution image reconstruction algorithm based on dictionary learning is proposed. First, the reconstructed image grid is refined and the area weight model is used to achieve accurate modeling of the projection process. Then, high quality images are selected as the training samples. The K-means singular value decomposition(K-SVD)algorithm is adopted to train the image dictionary. On the basis of the image dictionary, the orthogonal matching pursuit algorithm is used to implement sparse representation of the reconstructed image, which is introduced into the objective function of the reconstruction algorithm as a sparse constraint. Finally, the gradient descent method is adopted to solve the objective function. The experimental results show that compared to the conventional interpolation-based super-resolution reconstruction algorithms, the proposed algorithm has advantages on the image contrast and the edge preservation, and retains more high frequency information of images, effectively improving the spatial resolution of the reconstructed images.

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备注/Memo

备注/Memo:
收稿日期: 2016-01-06.
作者简介: 姚佳丽(1991—),女,硕士生;罗守华(联系人),男,博士,副教授,luoshouhua@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(61127002,61179035).
引用本文: 姚佳丽,李中源,吴华珍,等.基于字典学习的超分辨率显微CT图像重建[J].东南大学学报(自然科学版),2016,46(5):957-963. DOI:10.3969/j.issn.1001-0505.2016.05.010.
更新日期/Last Update: 2016-09-20