Performance of optical flow techniques
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Image Motion Estimation From Motion Smear-A New Computational Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identification of blur parameters from motion blurred images
Graphical Models and Image Processing
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Computer Methods for Mathematical Computations
Computer Methods for Mathematical Computations
Estimating image motion from smear: a sensor system and extensions
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Motion-Based Motion Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two motion-blurred images are better than one
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
Symmetrical Dense Optical Flow Estimation with Occlusions Detection
International Journal of Computer Vision
Image and video matting: a survey
Foundations and Trends® in Computer Graphics and Vision
Particle Video: Long-Range Motion Estimation Using Point Trajectories
International Journal of Computer Vision
Moving gradients: a path-based method for plausible image interpolation
ACM SIGGRAPH 2009 papers
Invertible motion blur in video
ACM SIGGRAPH 2009 papers
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Motion Field Estimation from Alternate Exposure Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilateral filtering-based optical flow estimation with occlusion detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Filtering requirements for gradient-based optical flow measurement
IEEE Transactions on Image Processing
Optical flow estimation using temporally oversampled video
IEEE Transactions on Image Processing
On performance analysis of optical flow algorithms
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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Most algorithms for dense 2D motion estimation assume pairs of images that are acquired with an idealized, infinitively short exposure time. In this work we compare two approaches that use an additional, motion-blurred image of a scene to estimate highly accurate, dense correspondence fields. We consider video sequences that are acquired with alternating exposure times so that a short-exposure image is followed by a long-exposure image that exhibits motion-blur. For both motion estimation algorithms we employ an image formation model that relates the motion blurred image to two enframing short-exposure images. With this model we can decipher the motion information encoded in the long-exposure image, but also estimate occlusion timings which are a prerequisite for artifact-free frame interpolation. The first approach solves for the motion in a pointwise least squares formulation while the second formulates a global, total variation regularized problem. Both approaches are evaluated in detail and compared to each other and state-of-the-art motion estimation algorithms.