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 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.

Traditional optic flow algorithms rely on consecutive short-exposure images. In contrast, long-exposed images contain integrated motion information directly in form of motion blur. In this paper, we show how the additional information provided by a long exposure image can be used to improve robustness and accuracy of motion field estimation. Recently, an image formation model was introduced [23] that relates a long-exposure image to preceding and succeeding short-exposure images in terms of dense 2D motion and occlusion. We formulate the original two-step problem for motion and occlusion timings as a joint minimization problem and derive a global TV-L1 energy functional that can be minimized efficiently and accurately. The approach is able to calculate highly accurate motion fields, assigning motion to occluded and disoccluded image regions in one joint optimization procedure.

This paper presents an extension to optical flow-based motion estimation using alternating short- and long- exposed images. While traditional optical flow algorithms rely on consecutive short-exposed images only, long-exposed images capture motion directly in the form of motion blur. This additional information can be used to achieve more robust and accurate motion field estimation as well as to extract the moment of occlusion. We introduce an image formation model that relates the long-exposed image to its preceding and succeeding short-exposed images in terms of dense 2D motion and per-pixel occlusion/disocclusion timings. Based on this image formation model, we describe a practical algorithm to estimate the motion field not only for completely visible image regions but also for pixels becoming occluded. For these pixels the Alternate Exposure Flow (AEF) also determines the moment of occlusion. We describe the application of AEF in frame interpolation to demonstrate the advantage of the additional long exposure information.

In this paper, we present an extension to optical flow for estimating dense 2D motion fields with occlusion information. While optical flow computation is based on two consecutive short-exposure images, we make also use of an additional intermediate long-exposure image. This motion-blurred intermediate image captures scene motion directly. We introduce an image formation model that relates the long-exposure image to both short-exposure images in terms of dense 2D motion and local occlusion/disocclusion. Based on this image formation model, we describe a practical algorithm that enables estimating the 2D motion field of occluding and occluded regions as well as determining the instant of occlusion. Results for synthetic and real scenes demonstrate the validity of the proposed approach.

Multi-view video camera setups record many images that capture nearly the same scene at nearly the same instant in time. Neighboring images in a multi-video setup restrict the solution space between two images: correspondences between one pair of images must be in accordance with the correspondences to the neighboring images.
The concept of accordance or consistency for correspondences between three neighboring images can be employed in the estimation of dense optical flow and in the matching of sparse features between three images.

This work has been partially funded by the German Science Foundation, DFG MA2555/4-2.

We present a method for image interpolation which is able to create high-quality, perceptually convincing transitions between recorded images. By implementing concepts derived from human vision, the problem of a physically correct image interpolation is relaxed to an image interpolation that is perceived as physically correct by human observers. We find that it suffices to focus on exact edge correspondences, homogeneous regions and coherent motion to compute such solutions. In our user study we confirm the visual quality of the proposed image interpolation approach. We show how each aspect of our approach increases the perceived quality of the interpolation results, compare the results obtained by other methods and investigate the achieved quality for different types of scenes.