[1] Liu Qiegen, Zhang Minghui, Liang Dong, et al. Two-level Bregmanized method for image interpolationwith graph regularized sparse coding [J]. Journal of Southeast University (English Edition), 2013, 29 (4): 384-388. [doi:10.3969/j.issn.1003-7985.2013.04.006]

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Two-level Bregmanized method for image interpolationwith graph regularized sparse coding()

基于图结构正则化稀疏表示的双层伯格曼图像插值算法

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 4

Page:

384-388

Research Field:

Computer Science and Engineering

Publishing date:

2013-12-20

Info

Title:

Two-level Bregmanized method for image interpolationwith graph regularized sparse coding

基于图结构正则化稀疏表示的双层伯格曼图像插值算法

Author(s):

Liu Qiegen¹;  2;  Zhang Minghui¹;  Liang Dong²

¹Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
²Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

刘且根¹;  2;  张明辉¹;  梁栋²

¹南昌大学电子信息工程系, 南昌 330031; ²中国科学院深圳先进技术研究院劳特伯生物医学成像研究中心, 深圳 518055

Keywords:

image interpolation;  Bregman iterative method;  graph regularized sparse coding;  alternating direction method

图像插值;  伯格曼迭代法;  图结构正则化稀疏表示;  交替方向法

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2013.04.006

Abstract:

A two-level Bregmanized method with graph regularized sparse coding(TBGSC)is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inner-level Bregmanized method devotes to dictionary updating and sparse represention of small overlapping image patches. The introduced constraint of graph regularized sparse coding can capture local image features effectively, and consequently enables accurate reconstruction from highly undersampled partial data. Furthermore, modified sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge within a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can effectively reconstruct images and it outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.

为了提高图像插值的恢复效果, 提出了一种基于图结构正则化稀疏表示的双层伯格曼迭代算法. 该迭代算法的外层用于约束图像观测数据, 内层用于更新图像块的学习字典和稀疏表示系数. 引入的图结构正则化稀疏表示约束可以有效地自适应图像块的局部结构, 对于严重受损的情形也能得到精确的恢复结果. 此外, 在内层迭代中改进的稀疏表示和简洁的字典更新策略使算法能快速地趋于收敛. 数值实验结果表明, 所提出的算法可以有效地恢复图像, 在主观视觉效果和客观量化标准上要优于目前已有的算法.

References:

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[9] Liu Q G, Wang S S, Luo J H, et al. An augmented Lagrangian approach to general dictionary learning for image denoising [J]. Journal of Visual Communication and Image Representation, 2012, 23(5): 753-766.
[10] Yin W T, Osher S, Goldfarb D, et al. Bregman iterative algorithms for l₁-minimization with applications to compressed sensing [J]. SIAM Journal on Imaging Sciences, 2008, 1(1): 142-168.
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Memo

Memo:

Biography: Liu Qiegen(1983—), male, doctor, lecturer, liuqiegen@ncu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61362001, 61102043, 61262084, 20132BAB211030, 20122BAB211015), the Basic Research Program of Shenzhen(No.JC201104220219A).
Citation: Liu Qiegen, Zhang Minghui, Liang Dong.Two-level Bregmanized method for image interpolation with graph regularized sparse coding[J].Journal of Southeast University(English Edition), 2013, 29(4):384-388.[doi:10.3969/j.issn.1003-7985.2013.04.006]

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