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Nonconvex Total Generalized Variation Model for Image Inpainting
Volume 32, Issue 2 (2021), pp. 357–370
Xinwu Liu  

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https://doi.org/10.15388/20-INFOR438
Pub. online: 8 December 2020      Type: Research Article      Open accessOpen Access

Received
1 May 2020
Accepted
1 November 2020
Published
8 December 2020

Abstract

It is a challenging task to prevent the staircase effect and simultaneously preserve sharp edges in image inpainting. For this purpose, we present a novel nonconvex extension model that closely incorporates the advantages of total generalized variation and edge-enhancing nonconvex penalties. This improvement contributes to achieve the more natural restoration that exhibits smooth transitions without penalizing fine details. To efficiently seek the optimal solution of the resulting variational model, we develop a fast primal-dual method by combining the iteratively reweighted algorithm. Several experimental results, with respect to visual effects and restoration accuracy, show the excellent image inpainting performance of our proposed strategy over the existing powerful competitors.

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Open access article under the CC BY license.

Keywords
image inpainting nonconvex function total generalized variation primal-dual method

Funding
This work was supported by National Natural Science Foundation of China (61402166), Scientific Research Fund of Hunan Provincial Education Department (19B215) and Hunan Provincial Natural Science Foundation of China (2020JJ4285).

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