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Deinterlacing with Motion-Compensated Anisotropic Diffusion

  • Conference paper
Statistical and Geometrical Approaches to Visual Motion Analysis

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5604))

Abstract

We present a novel deinterlacing scheme that makes consequent use of discontinuity-preserving partial differential equations (PDEs). It combines the accuracy of recent variational motion estimation techniques with the directional interpolation qualities of anisotropic diffusion filters. Our algorithm proceeds in three steps: First, we interpolate the interlaced images by means of a spatial edge enhancing diffusion process (EED). Then we apply the variational optic flow technique of Brox et al. (2004) in order to obtain a precise interframe registration. Finally we use a spatiotemporal generalisation of EED for motion-compensated inpainting of the missing data in the original sequence. Experiments demonstrate that the proposed method outperforms not only classical deinterlacing schemes, but also a recent PDE-based approach.

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Ghodstinat, M., Bruhn, A., Weickert, J. (2009). Deinterlacing with Motion-Compensated Anisotropic Diffusion. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds) Statistical and Geometrical Approaches to Visual Motion Analysis. Lecture Notes in Computer Science, vol 5604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03061-1_5

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  • DOI: https://doi.org/10.1007/978-3-642-03061-1_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03060-4

  • Online ISBN: 978-3-642-03061-1

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