Digital Image Restoration Using Conditional Markov Models.

Abstract

We are interested in developing digital image restoration algorithms using a class of spatial interaction models known as conditional Markov models. Our approach is to represent the images by Markov models on toroidal lattices and develop minimum mean square error (MMSE) restoration algorithms. The algorithms are non-recursive in structure and due to the underlying representation on toroidal lattices can be implemented using FFT algorithms. We give two types of algorithms. First we assume that a prototype of the original image is available and develop algorithms for restoration of degraded images. The degradation is assumed to be due to a known space invariant, non-separable, periodic point spread function and additive white noise. Secondly, we consider the more general situation when a prototype image is not available and give algorithms for MMSE filtering of noisy images. Experimental results are given for the above cases. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1981
Accession Number
ADA100002

Entities

People

  • R. Chellappa

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Air Force
  • Algorithms
  • Computational Complexity
  • Computer Science
  • Covariance
  • Degradation
  • Digital Images
  • Filters
  • Filtration
  • Image Processing
  • Image Restoration
  • Markov Models
  • Models
  • Noise
  • Prototypes
  • White Noise

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Statistical inference.

Technology Areas

  • Space
  • Space - Space Objects