This paper extends the classical warping-based optical flow method to achieve accurate flow in the presence of spatially-varying motion blur. Our idea is to parameterize the appearance of each frame as a function of both the pixel motion and the motion-induced blur. We search for the flows that best match two consecutive frames, which amounts to finding the derivative of a blurred frame with respect to both the motion and the blur, where the blur itself is a function of the motion. We propose an efficient technique to calculate the derivatives using pre-filtering. Our technique avoids performing spatially-varying filtering (which can be computationally expensive) during the optimization iterations. In the end, our derivative calculation technique can be easily incorporated with classical flow code to handle video with non-uniform motion blur with little performance penalty. Our method is evaluated on both synthetic and real videos and outperforms conventional flow methods in the presence of motion blur.
As one of the first treatments of blur in conjunction with optical flow, REF can calculate accurate flow in the presence of spatially-varying motion blur.
Optical flow in the presence of spatially-varying motion blur
2012 IEEE Conference on Computer Vision and Pattern Recognition
2012 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science