Image restoration using a universal GMM learning and adaptive wiener filter

Nobumoto Yamane, Motohiro Tabuchi, Yoshitaka Morikawa

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, an image restoration method using the Wiener filter is proposed. In order to bring the theory of the Wiener filter consistent with images that have spatially varying statistics, the proposed method adopts the locally adaptive Wiener filter (AWF) based on the universal Gaussian mixture distribution model (UNI-GMM) previously proposed for denoising. Applying the UNI-GMM-AWF for deconvolution problem, the proposed method employs the stationary Wiener filter (SWF) as a pre-filter. The SWF in the discrete cosine transform domain shrinks the blur point spread function and facilitates the modeling and filtering at the proceeding AWF. The SWF and UNI-GMM are learned using a generic training image set and the proposed method is tuned toward the image set. Simulation results are presented to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)2560-2571
Number of pages12
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE92-A
Issue number10
DOIs
Publication statusPublished - Oct 2009

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Keywords

  • Adaptive filter
  • Gaussian mixture distribution model
  • Optimal restoration filter
  • Statistical learning
  • Wiener filter

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics
  • Signal Processing

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