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Machine Vision and Applications
Published in: Machine Vision and Applications 1-2/2017

19-10-2016 | Original Paper

Low-rank image completion with entropy features

Authors: Junjie Cao, Jun Zhou, Xiuping Liu, Weiming Wang, Pingping Tao, Jun Wang

Published in: Machine Vision and Applications | Issue 1-2/2017

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Abstract

In this paper, we propose a novel method to complete the images or textures with the property of low rank. Our method leverages saliency detection with two entropy features to estimate initial corrupted regions. Then an iterative optimization model for low-rank and sparse errors recovery is designed to complete the corrupted images. Our iterative model can improve the initial corrupted regions and generate accurate and continuous corrupted regions via fully connected CRFs. By introducing a F-norm term in our model to absorb small noise, we can generate completed images which are more precise and have lower rank. Experiments indicate that our method introduces less local distortions than example-based methods for images with regular structures. It is also superior to the previous low-rank image completion method especially when the images contain low-rank corrupted regions. Furthermore, we show that the entropy features benefit the existing saliency detection methods too.
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Appendix
Available only for authorised users
Literature
1.
Ballester, C., Caselles, V., Verdera, J., Bertalmio, M., Sapiro, G.: A variational model for filling-in gray level and color images. In: IEEE International Conference on Computer Vision, pp. 10–16 (2001)
2.
Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 341–352 (2009)CrossRef
3.
Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp. 417–424 (2000)
4.
Bertalmio, M., Vese, L., Sapiro, G., Osher, S.: Simultaneous structure and texture image inpainting. IEEE Trans. Image Process. 12(8), 882–889 (2003)CrossRef
5.
6.
Criminisi, A., Pérez, P., Toyama, K.: Object removal by exemplar based inpainting. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 721–728 (2003)
7.
Darabi, S., Shechtman, E., Barnes, C., Goldman, D.B., Sen, P.: Image melding: combining inconsistent images using patch-based synthesis. ACM Trans. Graph. 31(4), 13–15 (2012)CrossRef
8.
Drori, I., Cohen-Or, D., Yeshurun, H.: Fragment-based image completion. ACM Trans. Graph. 22(3), 303–312 (2003)CrossRef
9.
Efros, A., Leung, T. K.: Texture synthesis by non-parametric sampling. In: The Proceedings of the 7th IEEE International Conference on Computer Vision (1999)
10.
Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp. 341–346 (2001)
11.
He, K., Sun, J.: Image completion approaches using the statistics of similar patches. IEEE Trans. Pattern Anal. Mach. Intell. 36(12), 2423–2435 (2014)CrossRef
12.
Huang, J.B., Kang, S.B., Ahuja, N., Kopf, J.: Image completion using planar structure guidance. ACM Trans. Graph. 33(4), 1–10 (2014)
13.
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: Computer Vision and Pattern Recognition, pp. 109–117 (2012)
14.
Liang, X., Ren, X., Zhang, Z., Ma, Y.: Repairing sparse low-rank texture. Eur. Conf. Comput. Vis. 7576(1), 482–495 (2012)
15.
Pritch, Y., Kav-Venaki, E., Peleg, S.: Shift-map image editing. In: 2009 IEEE 12th International Conference on Computer Vision (2009)
16.
Qin, Y., Lu, H.C., Xu, Y.Z., Wang, H.: Saliency detection via cellular automata. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
17.
Ting, H., Chen, S., Liu, J., Tang, X.: Image inpainting by global structure and texture propagation. In: Proceedings of the 15th international conference on Multimedia, pp. 517–520 (2007)
18.
Wexler, Y., Shechtman, E., Irani, M.: Spacetime completion of video. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 463–476 (2007)CrossRef
19.
Yang, C., Yang, M.S., Zhang, L.H., Lu, H.C.: Saliency detection via graph-based manifold ranking. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2009)
20.
Zhou, Z., Li, X., Wright, J., Candes, E., Ma, Y.: Stable principal component pursuit. In: IEEE International Symposium on Information Theory Proceedings, 2010 41(3), 1518–1522 (2010)
Metadata
Title
Low-rank image completion with entropy features
Authors
Junjie Cao
Jun Zhou
Xiuping Liu
Weiming Wang
Pingping Tao
Jun Wang
Publication date
19-10-2016
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 1-2/2017
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-016-0811-5

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