An edge driven wavelet frame model for image restoration

Jae Kyu Choi, Bin Dong, Xiaoqun Zhang

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images. In this paper, we introduce a new edge driven wavelet frame model for image restoration by approximating images as piecewise smooth functions. With an implicit representation of image singularities sets, the proposed model inflicts different strength of regularization on smooth and singular image regions and edges. The proposed edge driven model is robust to both image approximation and singularity estimation. The implicit formulation also enables an asymptotic analysis of the proposed models and a rigorous connection between the discrete model and a general continuous variational model. Finally, numerical results on image inpainting and deblurring show that the proposed model is compared favorably against several popular image restoration models.

Original languageEnglish (US)
JournalApplied and Computational Harmonic Analysis
DOIs
StateAccepted/In press - Jan 1 2018
Externally publishedYes

Keywords

  • (Tight) wavelet frames
  • Edge estimation
  • Framelets
  • Image restoration
  • Pointwise convergence
  • Variational method
  • Γ-convergence

ASJC Scopus subject areas

  • Applied Mathematics

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