Image Inpainting Based on Compressed Sensing

Fang Wang; Mei Hua Xie

doi:10.4028/www.scientific.net/AMR.317-319.2254

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 317-319Image Inpainting Based on Compressed Sensing

Image Inpainting Based on Compressed Sensing

Abstract:

An inpainting method is proposed to restore the image containing some missing patches. Based on the spartity of images, the method translates the problem of image inpainting into image reconstruction in compressed sensing. The inpainting model is solved using the split iteration. Simulated image inpainting results demonstrate the efficiency of the proposed method.

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Periodical:

Advanced Materials Research (Volumes 317-319)

Pages:

2254-2257

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.317-319.2254

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Online since:

August 2011

Authors:

Fang Wang, Mei Hua Xie

Keywords:

Compressed Sensing, Image Inpainting, Split Iteration, Variational Model

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References

[1] M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, Image inpainting, in Proceedings of SIGGRAPH 2000, 2000, New Orleans, LA.

DOI: 10.1145/344779.344972

Google Scholar

[2] M. Bertalmio, Strong-continuation, contrast- invariant inpainting with a third-order optimal PDE, IEEE Trans. Image Process., 15(7) (2006) 1934-1938.

DOI: 10.1109/tip.2006.877067

Google Scholar

[4] S. Bertozzi, Esedoglu, and A. Gillette, Inpainting of binary images using the Cahn–Hilliard equation, IEEE Trans. Image Process., 16(1) (2007) 285-291.

DOI: 10.1109/tip.2006.887728

Google Scholar

[5] J.-F. Cai, R. Chan, and Z. Shen, A framelet-based image inpainting algorithm, Appl. Comput. Harmon. A., 24 (2008) 131-149.

Google Scholar

[6] A. Telea, An image inpainting technique based on the fast marching method, J. Graph. Tools, 9(1) (2004) 23-34.

DOI: 10.1080/10867651.2004.10487596

Google Scholar

[7] F. Bornemann, T. Marz, Fast image inpainting based on coherence transport, J. Math. Imaging Vis, 28(2007) 259-278.

DOI: 10.1007/s10851-007-0017-6

Google Scholar

[8] M. Burger, L. He, C.-B. Schönlieb, Cahn- Hilliard, inpainting and a generalization for gray-value images, SIAM J. Imaging Sci. 2(4) (2009) 1129-1167.

DOI: 10.1137/080728548

Google Scholar

[9] M. Fornasier, C.-B. Schönlieb, Subspace correction methods for total variation and − minimization, SIAM J. Numer. Anal., 47(5) (2009) 3397-3428.

DOI: 10.1137/070710779

Google Scholar

[10] C.-B. Schonlieb, A. Bertozzi, M. Burger, and L, He, Image inpainting using a fourth-order total variation flow, 2009, UCLA CAM report 09-34.

Google Scholar

[11] D. Donoho, Compressed sensing, IEEE Trans. Inf. Theory, 52 (4) (2006) 1289-1306. E. Candès, J. Romberg, and T. Tao, Stable signal recovery from incomplete and inaccurate measurements, Commun. Pure Appl. Math., 59(8) (2006) 1207-1223.

DOI: 10.1002/cpa.20124

Google Scholar

[12] E. Candès, J. Romberg, and T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inf. Theory, 52 (2) (2006) 489-509.

DOI: 10.1109/tit.2005.862083

Google Scholar

[13] C. Zhu, Stable recovery of sparse signals via regularized minimization, IEEE Trans. Inf. Theory, 54(7) (2008) 336-3367.

DOI: 10.1109/tit.2008.924707

Google Scholar