Two algorithms for motion estimation from alternate exposure images
Proceedings of the 2010 international conference on Video Processing and Computational Video
Shape from Sharp and Motion-Blurred Image Pair
International Journal of Computer Vision
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Traditional optical flow algorithms rely on consecutive short-exposed images. In this work, we make use of an additional long-exposed image for motion field estimation. Long-exposed images integrate motion information directly in the form of motion-blur. With this additional information, more robust and accurate motion fields can be estimated. In addition, the moment of occlusion can be determined. Considering the basic signal-theoretical problem in motion field estimation, we exploit the fact that long-exposed images integrate motion information to prevent temporal aliasing. A suitable image formation model relates the long-exposed image to preceding and succeeding short-exposed images in terms of dense 2D motion and per-pixel occlusion/disocclusion timings. Based on our image formation model, we describe a practical variational algorithm to estimate the motion field not only for visible image regions but also for regions getting occluded. Results for synthetic as well as real-world scenes demonstrate the validity of the approach.