Improving alpha matting and motion blurred foreground estimation

We present a new method for separating motion blurred foreground objects from their background given a single image. Previous techniques focused on estimating alpha mattes for separating sharp, non-moving foreground objects from fairly homogeneous background. In those cases the only pixels which are ambiguous are those which exhibit fractional pixel occupancy. In this paper, we address the problem of alpha matte and foreground estimation of motion blurred objects. We show, that explicit modeling of the object motion facilitates the estimation and improves the quality of the estimated alpha mattes. In addition, we improve foreground extraction of motion blurred objects with a new regularization term. This task is particularly difficult in smeared out regions, where the background shimmers through. Both synthetic and real-world examples illustrate the merit of our approach.