Spatial Autoregressions in Digital Image Restoration: Simultaneous Models.

Abstract

We consider the application of spatial autoregressive random field models in the restoration of degraded images. The degradation is assumed to be due to a space invariant, periodic, nonseparable point spread function and additive noise, colored or white. We assume that the images are represented by two-dimensional spatial autoregressive models and we develop fast, optimal, non-recursive filters, the optimality criterion being the minimum mean squared error (MMSE). Within the class of spatial autoregressive models, there are two nonequivalent classes of random field (RF) models, the so-called simultaneous autoregressive (SAR) models and the conditional Markov (CM) models. In this paper, we develop restoration algorithms and give examples of restoration using the SAR models. The restoration filter is optimal, if the parameters characterizing the RF models are known exactly. In practice, however, they are estimated from the images. An iterative scheme is used for the estimation of parameters in SAR models. Performance bounds of restoration algorithms are calculated. In a subsequent paper, the case of CM models will be considered. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1980
Accession Number
ADA094354

Entities

People

  • R. Chellappa

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Algorithms
  • Computational Complexity
  • Computer Programs
  • Computer Science
  • Computers
  • Covariance
  • Digital Images
  • Eigenvalues
  • Filters
  • Image Processing
  • Image Restoration
  • Kalman Filters
  • Mathematical Filters
  • Noise
  • Recursive Filters
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Computer Vision.
  • Statistical inference.

Technology Areas

  • Space
  • Space - Space Objects