[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()

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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

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

Keywords:

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

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.

References:

[1] Hou H S, Andrews H C. Cubic splines for image interpolation and digital filtering [J]. IEEE Transactions on Acoustics Speech and Signal Processing, 1978, 26(6): 508-517.
[2] Takeda H, Farsiu S, Milanfar P. Kernel regression for image processing and reconstruction [J]. IEEE Transactions on Image Processing, 2007, 16(2): 349-366.
[3] Zhang X J, Wu X L. Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation [J]. IEEE Transactions on Image Processing, 2008, 17(6): 887-896.
[4] Liu X M, Zhao D B, Xiong R Q, et al. Image interpolation via regularized local linear regression [J]. IEEE Transactions on Image Processing, 2011, 20(12): 3455-3469.
[5] Li X. Patch-based image interpolation: algorithms and applications [C]//International Workshop on Local and Non-Local Approximation in Image Processing. Lausanne, Switzerland, 2008: 1-6.
[6] Dong W S, Zhang L, Lukac R, et al. Sparse representation based image interpolation with non-local autoregressive modeling [J]. IEEE Transactions on Image Processing, 2013, 22(4): 1382-1394.
[7] Zheng M, Bu J J, Chen C, et al. Graph regularized sparse coding for image representation [J]. IEEE Transactions on Image Processing, 2011, 20(5): 1327-1336.
[8] Afonso M, Dias J B, Figueiredo M. An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems [J]. IEEE Transactions on Image Processing, 2011, 20(3): 681-695.
[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.
[11] Xu J, Osher S. Iterative regularization and nonlinear inverse scale space applied to wavelet-based denoising [J]. IEEE Transactions on Image Processing, 2007, 16(2): 534-544.
[12] Dong W S, Li X, Zhang L, et al. Sparsity-based image denoising via dictionary learning and structural clustering [C]//IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO, USA, 2011: 457-464.

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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