Abstract
Optimisation of inpainting data plays an important role in inpainting-based codecs. For diffusion-based inpainting, it is well-known that a careful data selection has a substantial impact on the reconstruction quality. However, for exemplar-based inpainting, which is advantageous for highly textured images, no data optimisation strategies have been explored yet. In our paper, we propose the first data optimisation approach for exemplar-based inpainting. It densifies the known data iteratively: New data points are added by dithering the current error map. Afterwards, the data mask is further improved by nonlocal pixel exchanges. Experiments demonstrate that our method yields significant improvements for exemplar-based inpainting with sparse data.
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References
Adam, R.D., Peter, P., Weickert, J.: Denoising by inpainting. In: Lauze, F., Dong, Y., Dahl, A.B. (eds.) SSVM 2017. LNCS, vol. 10302, pp. 121–132. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58771-4_10
Arias, P., Facciolo, G., Caselles, V., Sapiro, G.: A variational framework for exemplar-based image inpainting. Int. J. Comput. Vis. 93(3), 319–347 (2011)
Belhachmi, Z., Bucur, D., Burgeth, B., Weickert, J.: How to choose interpolation data in images. SIAM J. Appl. Math. 70(1), 333–352 (2009)
Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of SIGGRAPH 2000, pp. 417–424. ACM Press, New Orleans, July 2000
Chen, Y., Ranftl, R., Pock, T.: A bi-level view of inpainting-based image compression. In: Kúkelová, Z., Heller, J. (eds.) Proceedings of Computer Vision Winter Workshop, Křtiny, Czech Republic, February 2014
Criminisi, A., Pérez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)
Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: Proceedings of IEEE International Conference on Computer Vision, vol. 2, pp. 1033–1038. IEEE, Kerkyra, September 1999
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). https://doi.org/10.1007/978-3-642-03641-5_25
Floyd, R.W., Steinberg, L.: An adaptive algorithm for spatial greyscale. Soc. Inf. Disp. 17(2), 75–77 (1976)
Galić, I., Weickert, J., Welk, M., Bruhn, A., Belyaev, A., Seidel, H.P.: Image compression with anisotropic diffusion. J. Math. Imaging Vis. 31(2–3), 255–269 (2008)
Guillemot, C., Meur, O.L.: Image inpainting: overview and recent advances. IEEE Signal Process. Mag. 31(1), 127–144 (2014)
Heeger, D.J., Bergen, J.R.: Pyramid-based texture analysis/synthesis. In: Proceedings of SIGGRAPH 1995, pp. 229–238. ACM Press, Los Angeles, July 1995
Hoeltgen, L., Setzer, S., Weickert, J.: An optimal control approach to find sparse data for laplace interpolation. In: Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., Tai, X.-C. (eds.) EMMCVPR 2013. LNCS, vol. 8081, pp. 151–164. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40395-8_12
Hoffmann, S., Mainberger, M., Weickert, J., Puhl, M.: Compression of depth maps with segment-based homogeneous diffusion. In: Kuijper, A., Bredies, K., Pock, T., Bischof, H. (eds.) SSVM 2013. LNCS, vol. 7893, pp. 319–330. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38267-3_27
Liang, L., Liu, C., Xu, Y.Q., Guo, B., Shum, H.Y.: Real-time texture synthesis by patch-based sampling. ACM Trans. Graph. 20(3), 127–150 (2001)
Mainberger, M., et al.: Optimising spatial and tonal data for homogeneous diffusion inpainting. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds.) SSVM 2011. LNCS, vol. 6667, pp. 26–37. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24785-9_3
Masnou, S., Morel, J.M.: Level lines based disocclusion. In: Proceedings of 1998 IEEE International Conference on Image Processing, Chicago, IL, vol. 3, pp. 259–263, October 1998
Peter, P., Kaufhold, L., Weickert, J.: Turning diffusion-based image colorization into efficient color compression. IEEE Trans. Image Process. 26(2), 860–869 (2017)
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, Cham (2015). https://doi.org/10.1007/978-3-319-18461-6_13
Schmaltz, C., Gwosdek, P., Bruhn, A., Weickert, J.: Electrostatic halftoning. Comput. Graph. Forum 29(8), 2313–2327 (2010)
Schmaltz, C., Peter, P., Mainberger, M., Ebel, F., Weickert, J., Bruhn, A.: Understanding, optimising and extending data compression with anisotropic diffusion. Int. J. Comput. Vis. 108(3), 222–240 (2014)
Schönlieb, C.B.: Partial Differential Equation Methods for Image Inpainting. Cambridge University Press, Cambridge (2015)
Ulichney, R.: Digital Halftoning. The MIT Press, London (1987)
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Part of our research has been funded by the ERC Advanced Grant INCOVID. This is gratefully acknowledged.
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Karos, L., Bheed, P., Peter, P., Weickert, J. (2018). Optimising Data for Exemplar-Based Inpainting. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_46
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