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Transport Based Image Morphing with Intensity Modulation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10302))

Abstract

We present a generalized optimal transport model in which the mass-preserving constraint for the \(L^2\)-Wasserstein distance is relaxed by introducing a source term in the continuity equation. The source term is also incorporated in the path energy by means of its squared \(L^2\)-norm in time of a functional with linear growth in space. This extension of the original transport model enables local density modulations, which is a desirable feature in applications such as image warping and blending. A key advantage of the use of a functional with linear growth in space is that it allows for singular sources and sinks, which can be supported on points or lines. On a technical level, the \(L^2\)-norm in time ensures a disintegration of the source in time, which we use to obtain the well-posedness of the model and the existence of geodesic paths. The numerical discretization is based on the proximal splitting approach [18] and selected numerical test cases show the potential of the proposed approach. Furthermore, the approach is applied to the warping and blending of textures.

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References

  1. Benamou, J.-D., Brenier, Y.: A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problem. Numer. Math. 84(3), 375–393 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  2. Burger, M., Franek, M., Schönlieb, C.-B.: Regularized regression and density estimation based on optimal transport. Appl. Math. Res. eXpress 2012(2), 209–253 (2012)

    MathSciNet  MATH  Google Scholar 

  3. Buttazzo, G., Santambrogio, F.: A model for the optimal planning of an urban area. SIAM J. Math. Anal. 37(2), 514–530 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  4. Carlier, G., Duval, V., Peyré, G., Schmitzer, B.: Convergence of entropic schemes for optimal transport, gradient flows. arXiv preprint arXiv:1512.02783 (2015)

  5. Caffarelli, L.A., McCann, R.J.: Free boundaries in optimal transport and monge-ampere obstacle problems. Ann. Math. 171, 673–730 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  6. Combettes, P.L., Pesquet, J.-C.: Proximal splitting methods in signal processing. In: Bauschke, H.H., Burachik, R.S., Combettes, P.L., Elser, V., Luke, D.R., Wolkowicz, H. (eds.) Fixed-Point Algorithms for Inverse Problems in Science and Engineering. SOA, vol. 49, pp. 185–212. Springer, Heidelberg (2011). doi:10.1007/978-1-4419-9569-8_10

    Chapter  Google Scholar 

  7. Chizat, L., Peyré, G., Schmitzer, B., Vialard, F.-X.: Unbalanced optimal transport: geometry and kantorovich formulation. arXiv preprint arXiv:1508.05216 (2015)

  8. Chizat, L., Schmitzer, B., Peyré, G., Vialard, F.-X.: An interpolating distance between optimal transport, Fischer-Rao. arXiv preprint arXiv:1506.06430 (2015)

  9. Dolbeault, J., Nazaret, B., Savaré, G.: A new class of transport distances between measures. Calc. Var. Partial Differ. Equ. 34(2), 193–231 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  10. Eckstein, J., Bertsekas, D.P.: On the douglas-rachford splitting method and the proximal point algorithm for maximal monotone operators. Math. Program. 55, 293–318 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  11. Esser, E.: Applications of lagrangian-based alternating direction methods and connections to split Bregman. CAM Rep. 9, 31 (2009)

    Google Scholar 

  12. Figalli, A.: The optimal partial transport problem. Arch. Ration. Mech. Anal. 195(2), 533–560 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Kondratyev, S., Monsaingeon, L., Vorotnikov, D.: A new optimal transport distance on the space of finite radon measures. arXiv preprint arXiv:1505.07746 (2015)

  14. Liero, M., Mielke, A., Savaré, G.: Optimal transport in competition with reaction: the Hellinger-Kantorovich distance and geodesic curves. Preprint no. 2160, WIAS (2015)

    Google Scholar 

  15. Maas, J., Rumpf, M., Schönlieb, C., Simon, S.: A generalized model for optimal transport of images including dissipation and density modulation. ESAIM Math. Model. Numer. Anal. 49(6), 1745–1769 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  16. Peyré, G.: Entropic Wasserstein gradient flows. arXiv preprint arXiv:1502.06216 (2015)

  17. Peyré, G., Fadili, J., Rabin, J.: Wasserstein active contours. In: Proceedings of IEEE International Conference on Image Processing, pp. 2541–2544 (2012)

    Google Scholar 

  18. Papadakis, N., Peyré, G., Oudet, E.: Optimal transport with proximal splitting. SIAM J. Imaging Sci. 7(1), 212–238 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. Piccoli, B., Rossi, F.: On properties of the generalized Wasserstein distance. arXiv preprint arXiv:1304.7014 (2013)

  20. Piccoli, B., Rossi, F.: Generalized Wasserstein distance and its application to transport equations with source. Arch. Ration. Mech. Anal. 211, 335–358 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  21. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  22. Schmitzer, B.: A sparse multi-scale algorithm for dense optimal transport. arXiv preprint arXiv:1510.05466 (2015)

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Correspondence to Stefan Simon .

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Maas, J., Rumpf, M., Simon, S. (2017). Transport Based Image Morphing with Intensity Modulation. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_45

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  • DOI: https://doi.org/10.1007/978-3-319-58771-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

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