Abstract
We consider the problem of interpolating frames in an image sequence. For this purpose accurate motion estimation can be very helpful. We propose to move the motion estimation from the surrounding frames directly to the unknown frame by parametrizing the optical flow objective function such that the interpolation assumption is directly modeled. This reparametrization is a powerful trick that results in a number of appealing properties, in particular the motion estimation becomes more robust to noise and large displacements, and the computational workload is more than halved compared to usual bidirectional methods. The proposed reparametrization is generic and can be applied to almost every existing algorithm. In this paper we illustrate its advantages by considering the classic TV-L 1 optical flow algorithm as a prototype. We demonstrate that this widely used method can produce results that are competitive with current state-of-the-art methods. Finally we show that the scheme can be implemented on graphics hardware such that it becomes possible to double the frame rate of 640×480 video footage at 30 fps, i.e. to perform frame doubling in realtime.
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References
Christensen, G.E., Johnson, H.J.: Consistent image registration. IEEE Transactions on Medical Imaging 20, 568–582 (2001)
Alvarez, L., Deriche, R., Papadopoulo, T., Sánchez, J.: Symmetrical dense optical flow estimation with occlusions detection. International Journal of Computer Vision 75, 371–385 (2007)
Alvarez, L., Castaño, C.A., García, M., Krissian, K., Mazorra, L., Salgado, A., Sánchez, J.: Symmetric Optical Flow. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2007. LNCS, vol. 4739, pp. 676–683. Springer, Heidelberg (2007)
Chen, W.: Surface velocity estimation from satellite imagery using displaced frame central difference equation. To appear in IEEE Transactions on Geoscience and Remote Sensing (2012)
Bresson, X., Chan, T.: Fast dual minimization of the vectorial total variation norm and application to color image processing. Inverse Problems and Imaging 2, 455–484 (2008)
Goldluecke, B., Strekalovskiy, E., Cremers, D.: The natural total variation which arises from geometric measure theory. SIAM Journal on Imaging Sciences 5, 537–563 (2012)
Zach, C., Pock, T., Bischof, H.: A Duality Based Approach for Realtime TV-L 1 Optical Flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214–223. Springer, Heidelberg (2007)
Rakêt, L.L., Roholm, L., Nielsen, M., Lauze, F.: TV-L 1 Optical Flow for Vector Valued Images. In: Boykov, Y., Kahl, F., Lempitsky, V., Schmidt, F.R. (eds.) EMMCVPR 2011. LNCS, vol. 6819, pp. 329–343. Springer, Heidelberg (2011)
Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. International Journal of Computer Vision 31, 1–31 (2011)
Herbst, E., Seitz, S., Baker, S.: Occlusion reasoning for temporal interpolation using optical flow. Technical Report UW-CSE-09-08-01, Department of Computer Science and Engineering, University of Washington (2009)
Werlberger, M., Pock, T., Unger, M., Bischof, H.: Optical Flow Guided TV-L1 Video Interpolation and Restoration. In: Boykov, Y., Kahl, F., Lempitsky, V., Schmidt, F.R. (eds.) EMMCVPR 2011. LNCS, vol. 6819, pp. 273–286. Springer, Heidelberg (2011)
Stich, T., Linz, C., Albuquerque, G., Magnor, M.: View and time interpolation in image space. Computer Graphics Forum 27, 1781–1787 (2008)
Huang, X., Rakêt, L.L., Luong, H.V., Nielsen, M., Lauze, F., Forchhammer, S.: Multi-hypothesis transform domain Wyner-Ziv video coding including optical flow. In: Multimedia Signal Processing (2011)
Keller, S., Lauze, F., Nielsen, M.: Temporal super resolution using variational methods. In: Mrak, M., Grgic, M., Kunt, M. (eds.) High-Quality Visual Experience: Creation, Processing and Interactivity of High-Resolution and High-Dimensional Video Signals. Springer (2010)
Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An Improved Algorithm for TV-1 Optical Flow. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 23–45. Springer, Heidelberg (2009)
Chen, K., Lorenz, D.: Image sequence interpolation using optimal control. Journal of Mathematical Imaging and Vision 41, 222–238 (2011)
Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. International Journal of Computer Vision 61, 211–231 (2005)
Weickert, J.: Theoretical foundations of anisotropic diffusion in image processing. In: Kropatsch, W.G., Klette, R., Solina, F. (eds.) Theoretical Foundations of Computer Vision, Computing Supplement, vol. 11, pp. 221–236. Springer (1994)
Galić, I., Weickert, J., Welk, M., Bruhn, A., Belyaev, A., Seidel, H.P.: Image compression with anisotropic diffusion. Journal of Mathematical Imaging and Vision 31, 255–269 (2008)
Ghodstinat, M., Bruhn, A., Weickert, J.: Deinterlacing with Motion-Compensated Anisotropic Diffusion. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 91–106. Springer, Heidelberg (2009)
Volz, S., Bruhn, A., Valgaerts, L., Zimmer, H.: Modeling temporal coherence for optical flow. In: Metaxas, D.N., Quan, L., Sanfeliu, A., Gool, L.J.V. (eds.) IEEE International Conference on Computer Vision (ICCV), pp. 1116–1123 (2011)
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Rakêt, L.L., Roholm, L., Bruhn, A., Weickert, J. (2012). Motion Compensated Frame Interpolation with a Symmetric Optical Flow Constraint. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_43
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DOI: https://doi.org/10.1007/978-3-642-33179-4_43
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