Proceedings Article10.1109/CVPR.2006.141
Image Completion Using Global Optimization
Nikos Komodakis
- 17 Jun 2006
- Vol. 1, pp 442-452
TL;DR: A new exemplar-based framework unifying image completion, texture synthesis and image inpainting is presented, which carries two very important extensions over standard belief propagation (BP): "prioritybased message scheduling" and "dynamic label pruning".
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Abstract: A new exemplar-based framework unifying image completion, texture synthesis and image inpainting is presented in this work. Contrary to existing greedy techniques, these tasks are posed in the form of a discrete global optimization problem with a well defined objective function. For solving this problem a novel optimization scheme, called Priority- BP, is proposed which carries two very important extensions over standard belief propagation (BP): "prioritybased message scheduling" and "dynamic label pruning". These two extensions work in cooperation to deal with the intolerable computational cost of BP caused by the huge number of existing labels. Moreover, both extensions are generic and can therefore be applied to any MRF energy function as well. The effectiveness of our method is demonstrated on a wide variety of image completion examples.
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Citations
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TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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Image inpainting
Marcelo Bertalmío,Guillermo Sapiro,V. Caselles,Coloma Ballester +3 more
- 01 Jul 2000
TL;DR: A novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators, and does not require the user to specify where the novel information comes from.
4.4K
Texture synthesis by non-parametric sampling
Alexei A. Efros,Thomas Leung +1 more
- 20 Sep 1999
TL;DR: A non-parametric method for texture synthesis that aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
TL;DR: A universal statistical model for texture images in the context of an overcomplete complex wavelet transform is presented, demonstrating the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set.
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