Context-aware patch-based image inpainting using Markov random field modeling
| Autor: | Tijana Ruzic, Aleksandra Pizurica |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Context model
Markov random field Optimization problem business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Inpainting Pattern recognition Context (language use) Image segmentation Belief propagation Computer Graphics and Computer-Aided Design Histogram Computer vision Artificial intelligence business Software Mathematics |
| Zdroj: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 24(1) |
| ISSN: | 1941-0042 |
| Popis: | 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 nonlocal 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 neighboring 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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