Learning reaction-diffusion models for image inpainting

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9358, Issue , 2015, Pages 356-367

  Yu, Wei ^a   Heber, Stefan ^a   Pock, Thomas ^a,b  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b AIT AUSTRIAN INSTITUTE OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING; PATTERN RECOGNITION;

DIFFUSION MODEL; IMAGE INPAINTING; INFLUENCE FUNCTIONS; LINEAR FILTERS; REACTION-DIFFUSION MODELS; STATE OF THE ART; STRUCTURAL SIMILARITY; TEXTURE PRESERVING;

DIFFUSION;

EID: 84952317476     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-24947-6_29     Document Type: Conference Paper

References (41)

1. Bertalmio, M., Vese, L., Sapiro, G., Osher, S.: Simultaneous structure and texture image inpainting. In: Proceedings of CVPR, vol. 2, pp. II-707-12 (2003)
2. Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Conference on Computer graphics and interactive techniques, pp. 417-424. ACM Press/Addison-Wesley Publishing Co. (2000)
3. Bugeau, A., Bertalmio, M., Caselles, V., Sapiro, G.: A comprehensive framework for image inpainting. Image Process. 19(10), 2634-2645 (2010)
4. Cao, F., Gousseau, Y., Masnou, S., Perez, P.: Geometrically guided exemplar-based inpainting. SIAM J. Img. Sci. 4(4), 1143-1179 (2011)
5. Caselles, V.: Exemplar-based image inpainting and applications. SIAM News 44(10), 1-3 (2011)
6. Chan, T.F., Shen, J.: Local inpainting models and TV inpainting. SIAM J. Appl. Math. 62(3), 1019-1043 (2001)
7. Chan, T.F., Shen, J.: Nontexture inpainting by curvature-driven diffusions. J. Vis. Commun. Image Represent. 12(4), 436-449 (2001)
8. Chen, Y., Ranftl, R., Pock, T.: A bi-level view of inpainting-based image compression (2014). arXiv preprint arXiv:1401.4112
9. Chen, Y., Ranftl, R., Pock, T.: Insights into analysis operator learning: from patch- based sparse models to higher order MRFs. Image Process. 23(3), 1060-1072 (2014)
10. Chen, Y., Yu, W., Pock, T.: On learning optimized reaction diffusion processes for effective image restoration. In: Proceeding of CVPR (2015)
11. Criminisi, A., Perez, P., Toyama, K.: Region filling and object removal by exemplar- based image inpainting. Trans. Img. Proc. 13(9), 1200-1212 (2004)
12. Efros, A., Leung, T.: Texture synthesis by non-parametric sampling. In: Proceeding of ICCV, vol. 2, pp. 1033-1038 (1999)
13. Esedoglu, S., Shen, J.: Digital inpainting based on the Mumford-Shah-Euler image model. Eur. J. Appl. Math. 13(04), 353-370 (2002)
14. Facciolo, G., Arias, P., Caselles, V., Sapiro, G.: Exemplar-based interpolation of sparsely sampled images. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds.) EMMCVPR 2009. LNCS, vol. 5681, pp. 331-344. Springer, Heidelberg (2009)
15. Galic, I., Weickert, J., Welk, M., Bruhn, A., Belyaev, A., Seidel, H.P.: Image compression with anisotropic diffusion. JMIV 31(2-3), 255-269 (2008)
16. Getreuer, P.: Total variation inpainting using split bregman. Image Process. On Line 2, 147-157 (2012)
17. Grossauer, H.: A combined PDE and texture synthesis approach to inpainting. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3022, pp. 214-224. Springer, Heidelberg (2004)
18. Hel-Or, Y., Shaked, D.: A discriminative approach for wavelet denoising. IEEE Trans. Image Process. 17(4), 443-457 (2008)
19. Herling, J., Broll, W.: Pixmix: A real-time approach to high-quality diminished reality. In: International Symposium on Mixed and Augmented Reality (ISMAR), pp. 141-150. IEEE (2012)
20. Jain, V., Seung, S.: Natural image denoising with convolutional networks. In: Advances in Neural Information Processing Systems, pp. 769-776 (2009)
21. Kokaram, A.C., Morris, R.D., Fitzgerald, W.J., Rayner, P.J.: Interpolation of missing data in image sequences. Image Process. 4(11), 1509-1519 (1995)
22. Komodakis, N., Tziritas, G.: Image completion using efficient belief propagation via priority scheduling and dynamic pruning. Trans. Img. Proc. 16(11), 2649-2661 (2007)
23. Kong, X., Li, K., Yang, Q., Wenyin, L., Yang, M.H.: A new image quality metric for image auto-denoising. In: Proceeding of ICCV, pp. 2888-2895. IEEE (2013)
24. Levin, A., Zomet, A., Weiss, Y.: Learning how to inpaint from global image statistics. In: Poceeding of ICCV, pp. 305-312 (2003)
25. Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. Image Process. 21(4), 1500-1512 (2012)
26. Liu, D., Sun, X., Wu, F., Li, S., Zhang, Y.Q.: Image compression with edge-based inpainting. Circuits Syst. Video Technol. 17(10), 1273-1287 (2007)
27. Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45(1-3), 503-528 (1989)
28. Masnou, S., Morel, J.M.: Level lines based disocclusion. In: Proceeding of ICIP, vol. 3, pp. 259-263 (1998)
29. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42(5), 577-685 (1989)
30. Nikolaidis, N., Pitas, I.: Digital image processing in painting restoration and archiving. In: Proceeding of ICCV, vol. 1, pp. 586-589. IEEE (2001)
31. Peter, P., Weickert, J.: Compressing images with diffusion- and exemplar-based inpainting. In: Aujol, J.-F., Nikolova, M., Papadakis, N. (eds.) SSVM 2015. LNCS, vol. 9087, pp. 154-165. Springer, Heidelberg (2015)
32. Rehman, A., Wang, Z.: Ssim-based non-local means image denoising. In: Proceeding of ICIP, pp. 217-220. IEEE (2011)
33. Roth, S., Black, M.: Fields of experts: a framework for learning image priors. In: Proceeding of CVPR, vol. 2, pp. 860-867 (2005)
34. Roth, S., Black, M.: Steerable random fields. In: Proceeding of ICCV, pp. 1-8 (2007)
35. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(14), 259-268 (1992)
36. Schmidt, U., Gao, Q., Roth, S.: A generative perspective on MRFs in low-level vision. In: Proceeding of CVPR, pp. 1751-1758. IEEE (2010)
37. Tiefenbacher, P., Bogischef, V., Merget, D., Rigoll, G.: Subjective and objective evaluation of image inpainting quality. In: Proceeding of ICIP. IEEE (2015)
38. TUM-image inpainting database. http://www.mmk.ei.tum.de/tumiid/
39. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. Image Process. 13(4), 600-612 (2004)
40. Xu, Z., Sun, J.: Image inpainting by patch propagation using patch sparsity. Image Process. 19(5), 1153-1165 (2010)
41. Zhu, S.C., Mumford, D.: Prior learning and gibbs reaction-diffusion. Pattern Anal. Mach. Intell. 19(11), 1236-1250 (1997)