An iterative method for restoring noisy blurred images

A. K. Katsaggelos*, J. Biemond, R. M. Mersereau, R. W. Schafer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

This paper introduces a new iterative image restoration method which is capable of restoring noisy blurred images by incorporating a priori knowledge about the image and noise statistics into the iterative procedure. The iteration equation consists of a prediction part which is based on a noncausal image model description and an innovation part which is weighted by a gain factor. The gain is computed using a linear MSE optimization procedure and is updated at each step of the iteration. The convergence of the algorithm, the resolution of some convergence difficulties by using "reblurring," and methods for the introduction of physical constraints will be discussed. This image restoration scheme can be interpreted as an iterative procedure with a statistical constraint on the image data. Results of several experiments with noisy blurred data are presented to demonstrate the feasibility of this approach.

Original languageEnglish (US)
Pages (from-to)139-160
Number of pages22
JournalCircuits, Systems, and Signal Processing
Volume3
Issue number2
DOIs
StatePublished - Jun 1 1984

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics

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