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Open AccessArticle

Image Completion with Hybrid Interpolation in Tensor Representation

Department of Electronics, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
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Appl. Sci. 2020, 10(3), 797; https://doi.org/10.3390/app10030797
Received: 10 December 2019 / Revised: 11 January 2020 / Accepted: 20 January 2020 / Published: 22 January 2020
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information)
The issue of image completion has been developed considerably over the last two decades, and many computational strategies have been proposed to fill-in missing regions in an incomplete image. When the incomplete image contains many small-sized irregular missing areas, a good alternative seems to be the matrix or tensor decomposition algorithms that yield low-rank approximations. However, this approach uses heuristic rank adaptation techniques, especially for images with many details. To tackle the obstacles of low-rank completion methods, we propose to model the incomplete images with overlapping blocks of Tucker decomposition representations where the factor matrices are determined by a hybrid version of the Gaussian radial basis function and polynomial interpolation. The experiments, carried out for various image completion and resolution up-scaling problems, demonstrate that our approach considerably outperforms the baseline and state-of-the-art low-rank completion methods. View Full-Text
Keywords: image completion; tensor decomposition models; image interpolation; image up-scaling image completion; tensor decomposition models; image interpolation; image up-scaling
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Zdunek, R.; Sadowski, T. Image Completion with Hybrid Interpolation in Tensor Representation. Appl. Sci. 2020, 10, 797.

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