Two Step Variational Method for Subpixel Optical Flow Computation

We develop an algorithm for the super-resolution optical flow computation by combining variational super-resolution and the variational optical flow computation. Our method first computes the gradient and the spatial difference of a high resolution images from these of low resolution images directly, without computing any high resolution images. Second the algorithm computes optical flow of high resolution image using the results of the first step.

[1]  Nicholas Ayache,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I , 2007, MICCAI.

[2]  Horst Bischof,et al.  A Duality Based Algorithm for TV- L 1-Optical-Flow Image Registration , 2007, MICCAI.

[3]  Timo Kohlberger,et al.  Variational optical flow estimation for particle image velocimetry , 2005 .

[4]  Dante C. Youla,et al.  Generalized Image Restoration by the Method of Alternating Orthogonal Projections , 1978 .

[5]  Mark W. Schmidt,et al.  Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches , 2007, ECML.

[6]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[7]  David Suter,et al.  Motion estimation and vector splines , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Henry Stark,et al.  Image recovery: Theory and application , 1987 .

[9]  D. Suter.  Motion estimation and vector spline , 1994, CVPR 1994.

[10]  Sang Uk Lee,et al.  A hierarchical optical flow estimation algorithm based on the interlevel motion smoothness constraint , 1993, Pattern Recognit..

[11]  Fang Chen,et al.  Left ventricular motion reconstruction based on elastic vector splines , 2000, IEEE Transactions on Medical Imaging.

[12]  Joost N. Kok.  Machine Learning: ECML 2007, 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007, Proceedings , 2007, ECML.

[13]  Michael Unser,et al.  Elastic registration of biological images using vector-spline regularization , 2005, IEEE Transactions on Biomedical Engineering.

[14]  Steven S. Beauchemin,et al.  The computation of optical flow , 1995, CSUR.

[15]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[16]  Stephan Didas,et al.  Splines in Higher Order TV Regularization , 2006, International Journal of Computer Vision.

[17]  Horst Bischof,et al.  A Duality Based Approach for Realtime TV-L1 Optical Flow , 2007, DAGM-Symposium.

[18]  Thomas Brox,et al.  Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Highly Accurate Optic Flow Computation with Theoretically Justified Warping Highly Accurate Optic Flow Computation with Theoretically Justified Warping , 2022 .

[19]  U. Grenander,et al.  Computational anatomy: an emerging discipline , 1998 .

[20]  Joachim Weickert,et al.  Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint , 2001, Journal of Mathematical Imaging and Vision.

[21]  Nahum Kiryati,et al.  Coarse to over-fine optical flow estimation , 2007, Pattern Recognit..

[22]  G. Wahba,et al.  Some New Mathematical Methods for Variational Objective Analysis Using Splines and Cross Validation , 1980 .

[23]  Brendt Wohlberg,et al.  Efficient Minimization Method for a Generalized Total Variation Functional , 2009, IEEE Transactions on Image Processing.

[24]  Abderrahman Bouhamidi,et al.  Approximation of vectors fields by thin plate splines with tension , 2005, J. Approx. Theory.

[25]  L. Amodei,et al.  A vector spline approximation , 1991 .

[26]  Daniel Cremers,et al.  An Unbiased Second-Order Prior for High-Accuracy Motion Estimation , 2008, DAGM-Symposium.