Image inpainting using nonlocal texture matching and nonlinear filtering

Ding Ding, Sundaresh Ram, Jeffrey J. Rodriguez

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Nonlocal texture similarity and local intensity smoothness are both essential for solving most image inpainting problems. In this paper, we propose a novel image inpainting algorithm that is capable of reproducing the underlying textural details using a nonlocal texture measure and also smoothing pixel intensity seamlessly in order to achieve natural-looking inpainted images. For matching texture, we propose a Gaussian-weighted nonlocal texture similarity measure to obtain multiple candidate patches for each target patch. To compute the pixel intensity, we apply the α -trimmed mean filter to the candidate patches to inpaint the target patch pixel-by-pixel. The proposed algorithm is compared with four current image inpainting algorithms under different scenarios, including object removal, texture synthesis, and error concealment. Experimental results show that the proposed algorithm outperforms the existing algorithms when inpainting large missing regions in images with texture and geometric structures.

Original languageEnglish (US)
Article number8531678
Pages (from-to)1705-1719
Number of pages15
JournalIEEE Transactions on Image Processing
Volume28
Issue number4
DOIs
StatePublished - Apr 2019

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Keywords

  • Exemplar-based image inpainting
  • nonlocal texture matching
  • object removal
  • texture synthesis
  • α-trimmed mean filter

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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