Fast local image inpainting based on the Allen-Cahn model

Yibao Li, Darae Jeong, Jung Il Choi, Seunggyu Lee, Junseok Kim

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

In this paper, we propose a fast local image inpainting algorithm based on the Allen-Cahn model. The proposed algorithm is applied only on the inpainting domain and has two features. The first feature is that the pixel values in the inpainting domain are obtained by curvature-driven diffusions and utilizing the image information from the outside of the inpainting region. The second feature is that the pixel values outside of the inpainting region are the same as those in the original input image since we do not compute the outside of the inpainting region. Thus the proposed method is computationally efficient. We split the governing equation into one linear equation and one nonlinear equation by using an operator splitting technique. The linear equation is discretized by using a fully implicit scheme and the nonlinear equation is solved analytically. We prove the unconditional stability of the proposed scheme. To demonstrate the robustness and accuracy of the proposed method, various numerical results on real and synthetic images are presented.

Original languageEnglish
Pages (from-to)65-74
Number of pages10
JournalDigital Signal Processing: A Review Journal
Volume37
Issue number1
DOIs
Publication statusPublished - 2015

Bibliographical note

Funding Information:
The second author (D. Jeong) was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( 2014R1A6A3A01009812 ). The corresponding author (J.S. Kim) was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning ( NRF-2014R1A2A2A01003683 ). The authors are grateful to the reviewers whose valuable suggestions and comments significantly improved the quality of this paper.

Publisher Copyright:
© 2014 Elsevier Inc. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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