Image restoration using statistical wavelet models

Juan Liu, Pierre Moulin

Research output: Contribution to journalConference article

Abstract

In this paper, we propose an image restoration algorithm based on state-of-the-art wavelet domain statistical models. We present an efficient method to estimate the model parameters from the observations, and solve the restoration problem in orthonormal and translation-invariant (TI) wavelet domains. Substantial improvements over previous wavelet-based restoration methods are obtained. The use of a TI wavelet transform further enhances the restoration performance. We study the improvement from the viewpoint of Bayesian estimation theory and show that replacing an estimator with its TI version will reduce the expected risk if the signal and the degradation model are stationary.

Original languageEnglish (US)
Pages (from-to)20-33
Number of pages14
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4478
DOIs
StatePublished - Dec 1 2001
EventWavelets: Applications in Signal and Image Processing IX - San Diego, CA, United States
Duration: Jul 30 2001Aug 1 2001

Fingerprint

Image Restoration
Image reconstruction
Restoration
restoration
Wavelets
Invariant
Estimation Theory
Domain Model
Bayesian Estimation
Orthonormal
Wavelet transforms
Wavelet Transform
Statistical Model
Degradation
estimators
wavelet analysis
Model
Estimator
degradation
Estimate

Keywords

  • Bayesian risk
  • Image restoration
  • Overcomplete wavelet representation
  • Statistical modeling

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Image restoration using statistical wavelet models. / Liu, Juan; Moulin, Pierre.

In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 4478, 01.12.2001, p. 20-33.

Research output: Contribution to journalConference article

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