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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Optimising Spatial and Tonal Data for Homogeneous Diffusion Inpainting
verfasst von : Markus Mainberger, Sebastian Hoffmann, Joachim Weickert, Ching Hoo Tang, Daniel Johannsen, Frank Neumann, Benjamin Doerr
Erschienen in: Scale Space and Variational Methods in Computer Vision
Verlag: Springer Berlin Heidelberg
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Finding optimal inpainting data plays a key role in the field of image compression with partial differential equations (PDEs). In this paper, we optimise the spatial as well as the tonal data such that an image can be reconstructed with minimised error by means of discrete homogeneous diffusion inpainting. To optimise the spatial distribution of the inpainting data, we apply a probabilistic data sparsification followed by a nonlocal pixel exchange. Afterwards we optimise the grey values in these inpainting points in an exact way using a least squares approach. The resulting method allows almost perfect reconstructions with only 5% of all pixels. This demonstrates that a thorough data optimisation can compensate for most deficiencies of a suboptimal PDE interpolant.