Wavelet frame based blind image inpainting

Bin Dong, Hui Ji, Jia Li, Zuowei Shen, Yuhong Xu

Research output: Contribution to journalArticle

89 Scopus citations

Abstract

Image inpainting has been widely used in practice to repair damaged/missing pixels of given images. Most of the existing inpainting techniques require knowing beforehand where those damaged pixels are, either given as a priori or detected by some pre-processing. However, in certain applications, such information neither is available nor can be reliably pre-detected, e.g. removing random-valued impulse noise from images or removing certain scratches from archived photographs. This paper introduces a blind inpainting model to solve this type of problems, i.e., a model of simultaneously identifying and recovering damaged pixels of the given image. A tight frame based regularization approach is developed in this paper for such blind inpainting problems, and the resulted minimization problem is solved by the split Bregman algorithm first proposed by Goldstein and Osher (2009) [1]. The proposed blind inpainting method is applied to various challenging image restoration tasks, including recovering images that are blurry and damaged by scratches and removing image noise mixed with both Gaussian and random-valued impulse noise. The experiments show that our method is compared favorably against many available two-staged methods in these applications.

Original languageEnglish (US)
Pages (from-to)268-279
Number of pages12
JournalApplied and Computational Harmonic Analysis
Volume32
Issue number2
DOIs
StatePublished - Mar 1 2012

Keywords

  • Image inpainting
  • Sparse approximation
  • Split Bregman algorithm
  • Wavelet frame

ASJC Scopus subject areas

  • Applied Mathematics

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