Super-resolution of the alpha matte and the foreground object from a video are jointly attempted in this paper. Instead of super-resolving them independently, we treat super-resolution of the matte and foreground in a combined framework, incorporating the matting equation in the image degradation model. We take multiple adjacent frames from a low-resolution video with non-global motion for increasing the spatial resolution. This ill-posed problem is regularized by employing a Bayesian restoration approach, wherein the high-resolution image is modeled as a Markov Random Field. In matte super-resolution, it is particularly important to preserve fine details at the boundary pixels between the foreground and background. For this purpose, we use a discontinuity-adaptive smoothness prior to include observed data in the solution. This framework is useful in video editing applications for compositing low-resolution objects into high-resolution videos.
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