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Context-aware patch-based image inpainting using Markov random field modeling

Tijana Ruzic (UGent) and Aleksandra Pizurica (UGent)
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Abstract
In this paper, we first introduce a general approach for context-aware patch-based image inpainting, where textural descriptors are used to guide and accelerate the search for well-matching (candidate) patches. A novel top-down splitting procedure divides the image into variable size blocks according to their context, constraining thereby the search for candidate patches to non-local image regions with matching context. This approach can be employed to improve the speed and performance of virtually any (patch-based) inpainting method. We apply this approach to the so-called global image inpainting with the Markov random field (MRF) prior, where MRF encodes a priori knowledge about consistency of neighbouring image patches. We solve the resulting optimization problem with an efficient low-complexity inference method. Experimental results demonstrate the potential of the proposed approach in inpainting applications like scratch, text and object removal. Improvement and significant acceleration of a related global MRF-based inpainting method is also evident
Keywords
VIDEO, Inpainting, patch-based, FRAMEWORK, SCENE, Gabor filtering, ALGORITHM, RETRIEVAL, CLASSIFICATION, SEGMENTATION, COMPLETION, BELIEF-PROPAGATION, texture features, context-aware

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Citation

Please use this url to cite or link to this publication:

Chicago
Ruzic, Tijana, and Aleksandra Pizurica. 2015. “Context-aware Patch-based Image Inpainting Using Markov Random Field Modeling.” Ieee Transactions on Image Processing 24 (1): 444–456.
APA
Ruzic, T., & Pizurica, A. (2015). Context-aware patch-based image inpainting using Markov random field modeling. IEEE TRANSACTIONS ON IMAGE PROCESSING, 24(1), 444–456.
Vancouver
1.
Ruzic T, Pizurica A. Context-aware patch-based image inpainting using Markov random field modeling. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2015;24(1):444–56.
MLA
Ruzic, Tijana, and Aleksandra Pizurica. “Context-aware Patch-based Image Inpainting Using Markov Random Field Modeling.” IEEE TRANSACTIONS ON IMAGE PROCESSING 24.1 (2015): 444–456. Print.
@article{5968560,
  abstract     = {In this paper, we first introduce a general approach
for context-aware patch-based image inpainting, where textural
descriptors are used to guide and accelerate the search for
well-matching (candidate) patches. A novel top-down splitting
procedure divides the image into variable size blocks according
to their context, constraining thereby the search for candidate
patches to non-local image regions with matching context. This
approach can be employed to improve the speed and performance of virtually any (patch-based) inpainting method. We apply this approach to the so-called global image inpainting with the Markov random field (MRF) prior, where MRF encodes
a priori knowledge about consistency of neighbouring image
patches. We solve the resulting optimization problem with an
efficient low-complexity inference method. Experimental results
demonstrate the potential of the proposed approach in inpainting applications like scratch, text and object removal. Improvement and significant acceleration of a related global MRF-based inpainting method is also evident},
  author       = {Ruzic, Tijana and Pizurica, Aleksandra},
  issn         = {1057-7149},
  journal      = {IEEE TRANSACTIONS ON IMAGE PROCESSING},
  language     = {eng},
  number       = {1},
  pages        = {444--456},
  title        = {Context-aware patch-based image inpainting using Markov random field modeling},
  url          = {http://dx.doi.org/10.1109/TIP.2014.2372479},
  volume       = {24},
  year         = {2015},
}

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