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A conditional multiscale locally Gaussian texture synthesis algorithm. (English) Zbl 1409.94504
Summary: Exemplar-based texture synthesis is defined as the process of generating, from an input texture sample, new texture images that are perceptually equivalent to the input. In the present work, we model texture self-similarity with conditional Gaussian distributions in the patch space in order to extend the use of stitching techniques. Then, a multiscale texture synthesis algorithm is introduced, where texture patches are modeled at each scale as spatially variable Gaussian vectors in the patch space. The Gaussian distribution for each patch is inferred from the set of its nearest neighbors in the patch space obtained from the input sample. This approach is tested over several real and synthetic texture images, and its results show the effectiveness of the proposed technique for a wide range of textures.

MSC:
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
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[1] Aguerrebere, C; Gousseau, Y; Tartavel, G, Exemplar-based texture synthesis: the efros-leung algorithm, Image Process. On Line, 2013, 213-231, (2013)
[2] Ashikhmin, M.: Synthesizing natural textures. In: Proceedings of the 2001 Symposium on Interactive 3D Graphics, pp. 217-226. ACM, New York (2001)
[3] Barnes, C; Shechtman, E; Finkelstein, A; Goldman, D, Patchmatch: a randomized correspondence algorithm for structural image editing, ACM Trans. Graph. TOG, 28, 24, (2009)
[4] Briand, T; Vacher, J; Galerne, B; Rabin, J, The heeger & Bergen pyramid based texture synthesis algorithm, Image Process. On Line, 4, 276-299, (2014)
[5] Efros, A., Leung, T.K., et al.: Texture synthesis by non-parametric sampling. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 2, pp. 1033-1038. IEEE, Washington, DC(1999) · Zbl 1332.62371
[6] 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. ACM, New York (2001)
[7] Fedorov, V; Facciolo, G; Arias, P, Variational framework for non-local inpainting, Image Process. On Line, 5, 362-386, (2015)
[8] Galerne, B., Gousseau, Y., Morel, J.M.: Micro-texture synthesis by phase randomization. Image Process. On Line (2011). doi:10.5201/ipol.2011.ggm_rpn · Zbl 1372.94086
[9] Galerne, B; Gousseau, Y; Morel, JM, Random phase textures: theory and synthesis, IEEE Trans. Image Process., 20, 257-267, (2011) · Zbl 1372.94086
[10] Gatys, L.A., Ecker, A.S., Bethge, M.: Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks. arXiv preprint arXiv:1505.07376 (2015) · Zbl 1332.62371
[11] Heeger, D.J., Bergen, J.R.: Pyramid-based texture analysis/synthesis. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, pp. 229-238. ACM, New York (1995)
[12] Julesz, B, Visual pattern discrimination, IRE Trans. Inf. Theory, 8, 84-92, (1962)
[13] Kwatra, V; Essa, I; Bobick, A; Kwatra, N, Texture optimization for example-based synthesis, ACM Trans. Graph. (TOG), 24, 795-802, (2005)
[14] Kwatra, V; Schödl, A; Essa, I; Turk, G; Bobick, A, Graphcut textures: image and video synthesis using graph cuts, ACM Trans. Graph. (TOG), 22, 277-286, (2003)
[15] Lebrun, M; Buades, A; Morel, JM, Implementation of the “non-local bayes” (NL-Bayes) image denoising algorithm, Image Process. On Line, 3, 1-42, (2013)
[16] Lefebvre, S; Hoppe, H, Parallel controllable texture synthesis, ACM Trans. Graph. (TOG), 24, 777-786, (2005)
[17] Levina, E; Bickel, PJ, Texture synthesis and nonparametric resampling of random fields, Ann. Stat., 34, 1751-1773, (2006) · Zbl 1246.62194
[18] Liang, L; Liu, C; Xu, YQ; Guo, B; Shum, HY, Real-time texture synthesis by patch-based sampling, ACM Trans. Graph. (ToG), 20, 127-150, (2001)
[19] Morrison, D.F.: Multivariate Statistical Methods. McGraw-Hill, New York (1990) · Zbl 0183.20605
[20] Peyré, G, Sparse modeling of textures, J. Math. Imaging Vis., 34, 17-31, (2009)
[21] Portilla, J; Simoncelli, EP, A parametric texture model based on joint statistics of complex wavelet coefficients, Int. J. Comput. Vis., 40, 49-70, (2000) · Zbl 1012.68698
[22] Raad, L., Desolneux, A., Morel, J.M.: Locally gaussian exemplar based texture synthesis. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 4667-4671. IEEE, Paris (2014)
[23] Raad, L., Desolneux, A., Morel, J.M.: Conditional gaussian models for texture synthesis. In: Scale Space and Variational Methods in Computer Vision, pp. 474-485. Springer, Berlin (2015)
[24] Raad, L., Desolneux, A., Morel, J.M.: Multiscale exemplar based texture synthesis by locally gaussian models. In: Iberoamerican Congress on Pattern Recognition (CIARP) (2015) · Zbl 1409.94504
[25] Tartavel, G; Gousseau, Y; Peyré, G, Variational texture synthesis with sparsity and spectrum constraints, J. Math. Imaging Vis., 52, 124-144, (2014) · Zbl 1332.62371
[26] Wei, L.Y., Levoy, M.: Fast texture synthesis using tree-structured vector quantization. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 479-488. ACM Press/Addison-Wesley Publishing Co., New York (2000) · Zbl 1372.94086
[27] Wei, L.Y., Lefebvre, S., Kwatra, V., Turk, G.: State of the art in example-based texture synthesis. In: Eurographics 2009, State of the Art Report, EG-STAR, pp. 93-117. Eurographics Association, Munich (2009)
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