Single Image Interpolation Using Texture-Aware Low-Rank Regularization

GAO Zhirong; DING Lixin; XIONG Chengyi

doi:10.1049/cje.2017.08.025

Volume 27 Issue 2

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2018 > 27(2): 374-380

GAO Zhirong, DING Lixin, XIONG Chengyi, “Single Image Interpolation Using Texture-Aware Low-Rank Regularization,” Chinese Journal of Electronics, vol. 27, no. 2, pp. 374-380, 2018, doi: 10.1049/cje.2017.08.025

Citation:

GAO Zhirong, DING Lixin, XIONG Chengyi, “Single Image Interpolation Using Texture-Aware Low-Rank Regularization,” Chinese Journal of Electronics, vol. 27, no. 2, pp. 374-380, 2018, doi: 10.1049/cje.2017.08.025

Citation:

PDF( 826 KB)

Single Image Interpolation Using Texture-Aware Low-Rank Regularization

doi: 10.1049/cje.2017.08.025

1.
Computer School, Wuhan University, Wuhan 430072, China;
2.
College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China;
3.
Hubei Key Laboratory of Intelligent Wireless Communication, South-Central University for Nationalities, Wuhan 430074, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61471400).

More Information

Corresponding author: DING Lixin (corresponding author) was born in Hunan, China, in 1967. He received his Ph.D. degree in computer software and theory from State Key Laboratory of Software Engineering, Wuhan university, in 1998. Now he is a professor in State Key Laboratory of Software Engineering, Wuhan university. His research interests include Intelligent computing, intelligent information processing, machine learning, and cloud computing. (Email:lxding@whu.edu.cn)
Received Date: 2015-10-10
Rev Recd Date: 2016-07-18
Publish Date: 2018-03-10

Abstract

Abstract

A new image interpolation method is proposed by using the image priors of nonlocal self-similarity and low rank approximation. Here the traditional cubicspline interpolation is conducted to obtain an initial High resolution (HR) image. The nonlocal similar image patches are vectorized to form data matrices with low rank prior, and thus a low rank regularization term is embedded into the reconstruction model. The texture information measured by entropy of the data matrix is extracted and used to achieve adaptive low rank approximation for retaining the latent fine details of image. The Split bregman iteration (SBI) algorithm and weighted Partial singular values thresholding (PSVT) method are adopted to obtain the optimum solution of the reconstruction model. Experimental results demonstrate the effectiveness of the proposed method in improving image quality in terms of Peak signal to noise ratio (PSNR) and/or Structural similarity (SSIM).
- Image interpolation,
- Nonlocal selfsimilarity,
- Adaptive low rank approximation,
- Texture information,
- Weighted partial singular values thresholding

FullText(HTML)

References(33)

References

Z. Zhang, Q. Chen, Q. Peng, Q. Zhang and W. Li, "MAP-based single-frame super-resolution reconstruction for character image", Acta Electronica Sinica, Vol.43, No.1, pp.191-197, 2015. (in Chinese)

H.S. Hou and H.C. Andrews, "Cubic splines for image interpolation and digital filtering", IEEE Trans. Acoust., Speech, Signal Process., Vol.26, No.6, pp.508-517, 1978.

R.G. Keys, "Cubic convolution interpolation for digital image processing", IEEE Trans. Acoust., Speech, Signal Process., Vol.29, No.6, pp.1153-1160, 1981.

X. Li and M.T. Orchard, "New edge-directed interpolation", IEEE Trans. Image Process., Vol.10, No.10, pp.1521-1527, 2001.

L. Zhang and X. Wu, "An edge guided image interpolation algorithm via directional filtering and data fusion", IEEE Trans. Image Process., Vol.15, No.8, pp.2226-2238, 2006.

J. Yang, J. Wright, T. Huang and Y. Ma, "Image superresolution via sparse representation", IEEE Trans. Image Process., Vol.19, No.11, pp.2861-2873, 2010.

W. Dong, L. Zhang, R. Lukac, et al., "Sparse representation based image interpolation with nonlocal autoregressive modeling", IEEE Trans. on Image Process., Vol.22, No.4, pp.1382-1394, 2013.

Y. Romano, M. Protter and M. Elad, "Single image interpolation via adaptive non-local sparsity-based modeling", IEEE Transactions on Image Processing, Vol.23, No.7, pp.3085-3098, 2014.

B. Olshausen and D. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images", Nature, Vol.381, pp.607-609, 1996.

E. Candès, J. Romberg and T. Tao, "Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information", IEEE Trans. Inf. Theory, Vol.52, No.2, pp.489-509, 2006.

E. Candès and T. Tao, "Near optimal signal recovery from random projections:Universal encoding strategies?" IEEE Trans. Inf. Theory, Vol.52, No.12, pp.5406-5425, 2006.

K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, "Image denoising by sparse 3-D transform-domain collaborative filtering", IEEE Trans. Image Process., Vol.16, No.8, pp.2080-2095, 2007.

A. Danielyan, V. Katkovnik and K. Egiazarian, "BM3D frames and variational image deblurring", IEEE Trans. Image Process., Vol.21, No.4, pp.1715-1728, 2012.

M. Irani and S. Peleg, "Motion analysis for image enhancement:Resolution, occlusion, and transparency", J. Visual Commun. Image Represent., Vol.4, No.4, pp.324-335, 1993.

A. Marquina and S.J. Osher, "Image super-resolution by TV regularization and Bregman iteration", J. Sci. Comput., Vol.37, No.3, pp.367-382, 2008.

C. Zhao, X. Li, J. Ren, et al., "A novel framework for objectbased coding and compression of hyperspectral imagery", Chinese Journal of Electronics, Vol.24, No.2, pp.300-305, 2015.

T. Li, X. Tain, C. Xiong and J. Tian, "A coding scheme for noisy image based on layer segmentation", Chinese Journal of Electronics, Vol.25, No.4, pp.700-705, 2016.

L. Zhang and K. Zhang, "Weighted discriminative sparse coding for image classification", Chinese Journal of Electronics, Vol.23, No.1, pp.104-108, 2014.

X. He, F. Wang, W. Chai and L. Wu, "Ant colony clustering algorithm for underdetermined BSS", Chinese Journal of Electronics, Vol.22, No.2, pp.319-324, 2013.

J.A. Tropp and S.J. Wright, "Computational methods for sparse solution of linear inverse problems", Proc. IEEE, Vol.98, No.6, pp.948-958, 2010.

I. Daubechies, M. Defriese and C. DeMol, "An iterative thresholding algorithm for linear inverse problems with a sparsity constraint", Commun. Pure Appl. Math., Vol.57, No.11, pp.1413-1457, 2004.

P. Combettes and V. Wajs, "Signal recovery by proximal forwardbackward splitting", SIAM J. Multiscale Model. Simul., Vol.4, No.4, pp.1168-1200, 2005.

F. De la Torre and M. Black, "Robust principal component analysis for computer vision", Proc. of IEEE 8th Int. Conf. Comput. Vis., Vancouver, BC, Canada, pp.362-369, 2001.

F. De la Torre and M. Black, "A framework for robust subspace learning", Int. J. Comput. Vis., Vol.54, No.1, pp.117-142, 2003.

Q. Ke and T. Kanade, "Robust L1 norm factorization in the presence of outliers and missing data by alternative convex programming", Ph.D. Dissertation, Dept. Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA, 2005.

E. Candès, X. Li, Y. Ma and J. Wright, "Robust principal component analysis?" J. ACM, Vol.58, No.3, Article No.11, 2011.

Z. Lin, A. Ganesh, J. Wright, L. Wu, M. Chen and Y. Ma, "Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix", Technical Report, UILU-ENG-09-2214, 2009.

Z. Lin, M. Chen, L. Wu and Y. Ma, "The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices", Tech. Rep., UILU-ENG-09-2215, 2009.

J.F. Cai, E.J. Candes and Z. Shen, "A singular value thresholding algorithm for matrix completion", SIAM Journal on Optimization, Vol.20, No.4, pp.1956-1982, 2008.

Y. Sun, Z. Wu, M. Wu and Q. Liu, "Compressive sensing reconstruction of hyperspectral imagery jointly using low rank and prior", Acta Electronica Sinica, Vol.42, No.11, pp.2219-2224, 2014. (in Chinese)

S. Gu, L. Zhang, W. Zuo and X. Feng, "Weighted nuclear norm minimization with application to image denoising", IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp.2862-2869, 2014.

T. Goldstein and S. Osher, "The split Bregman algorithm for L1 regularized problems", SIAM J. Imag. Sci., Vol.2, No.2, pp.323-343, 2009.

T.-H. Oh, H. Kim, Y.-W. Tai, J.-C. Bazin and I.S. Kweon, "Partial sum minimization of singular values in RPCA for low-level vision", Proc. IEEE Int. Conf. Comput. Vis., Sydney, NSW, Australia, pp.145-152, 2013.

Relative Articles

Supplements(0)

Cited By

Proportional views