Optimising spatial and tonal data for homogeneous diffusion inpainting
Date
2012
Authors
Mainberger, M.
Hoffmann, S.
Weickert, J.
Tang, C.
Johannsen, D.
Neumann, F.
Doerr, B.
Editors
Bruckstein, A.M.
Romeny, B.M.T.
Bronstein, A.M.
Bronstein, M.M.
Romeny, B.M.T.
Bronstein, A.M.
Bronstein, M.M.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision, held in Ein-Gedi, Israel 29 May-2 June, 2011 / A.M Bruckstein, B.M. ter Haar Romeny, A.M. Bronstein and M.M. Bronstein (eds.), pp.26-37
Statement of Responsibility
Markus Mainberger, Sebastian Hoffmann, Joachim Weickert, Ching Hoo Tang, Daniel Johannsen, Frank Neumann and Benjamin Doerr
Conference Name
International Conference on Scale Space and Variational Methods in Computer Vision (3rd : 2011 : Ein-Gedi, Israel)
Abstract
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.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© Springer-Verlag Berlin Heidelberg 2012