Abstract
This paper presents a novel texture synthesis algorithm that performs a sparse expansion of the patches of the image in a dictionary learned from an input exemplar. The synthesized texture is computed through the minimization of a non-convex energy that takes into account several constraints. Our first contribution is the computation of a sparse expansion of the patches imposing that the dictionary atoms are used in the same proportions as in the exemplar. This is crucial to enable a fair representation of the features of the input image during the synthesis process. Our second contribution is the use of additional penalty terms in the variational formulation to maintain the histogram and the low frequency content of the input. Lastly we introduce a non-linear reconstruction process that stitches together patches without introducing blur. Numerical results illustrate the importance of each of these contributions to achieve state of the art texture synthesis.
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References
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1966)
Cross, G.R., Jain, A.K.: Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence (1), 25–39 (1983)
De Bonet, J.S.: Multiresolution sampling procedure for analysis and synthesis of texture images. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 361–368 (1997)
Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1033–1038. IEEE (1999)
Elad, M., Aharon, M.: Image denoising via learned dictionaries and sparse representation. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 895–900. IEEE (2006)
Galerne, B., Gousseau, Y., Morel, J.-M.: Random phase textures: Theory and synthesis. IEEE Transactions on Image Processing 20(1), 257–267 (2011)
Heeger, D.J., Bergen, J.R.: Pyramid-based texture analysis/synthesis. In: SIGGRAPH 1995, pp. 229–238 (1995)
Julesz, B.: A theory of preattentive texture discrimination based on first-order statistics of textons. Biological Cybernetics 41(2), 131–138 (1981)
Kwatra, V., Schodl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: Image and video synthesis using graph cuts. ACM Transactions on Graphics 22(3), 277–286 (2003)
Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 41(12), 3397–3415 (1993)
Peyré, G.: Sparse modeling of textures. Journal of Mathematical Imaging and Vision 34(1), 17–31 (2009)
Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision 40(1), 49–70 (2000)
Villani, C.: Topics in optimal transportation, vol. 58. Amer Mathematical Society (2003)
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. Eurographics Association (2009)
Wei, L.Y., Levoy, M.: Fast texture synthesis using tree-structured vector quantization. In: SIGGRAPH 2000, pp. 479–488. ACM Press/Addison-Wesley Publishing Co. (2000)
Zhu, S.C., Wu, Y., Mumford, D.: Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling. International Journal of Computer Vision 27(2), 107–126 (1998)
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Tartavel, G., Gousseau, Y., Peyré, G. (2013). Constrained Sparse Texture Synthesis. In: Kuijper, A., Bredies, K., Pock, T., Bischof, H. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2013. Lecture Notes in Computer Science, vol 7893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38267-3_16
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DOI: https://doi.org/10.1007/978-3-642-38267-3_16
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