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A new approach for image matting is proposed based on the Kalman filter, to extract the matte and original foreground, despite the presence of noise in the observed image. Different filter formulations with a discontinuity-adaptive Markov random field prior are proposed for handling additive white Gaussian noise and film-grain noise.
Reconstruction of a super-resolved image from multiple frames and extraction of matte are two popular topics that have been solved independently. In this paper, we advocate a unified framework that assimilates matting within the super-resolution model. We show that joint estimation is advantageous, as super-resolved edge information helps in obtaining a… (More)
Camouflaging an object in a photograph is normally performed with the intent of unnoticeably hiding it within a given image. In this work, we give a different dimension to this problem and raise the interesting issue of camouaging motion blur with special relevance to non-uniformly blurred images. Given a blurred photograph, we apply a suitably derived… (More)
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… (More)