Adaptive Image Estimation Using Reduced Update Filters,

Abstract

When an image is estimated from noisy data using a linear shift-invariant (LSI) filter, the subjective improvement is relatively poor at low signal-to-noise ratios. This occurs for at least two reasons: first, the statistics of the image are markedly space-variant and second, the eye is very sensitive to blurring of edges. However, adaptive filtering techniques can be applied to improve the subjective quality of noisy images even at low signal-to-noise ratios. This is accomplished in the present work by using multiple models to match the space-invariant statistics and by using oriented edge models to prevent edge blurring in the filtered result. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1980
Accession Number
ADA083988

Entities

People

  • H. Kaufman
  • J. W. Woods

Organizations

  • Rensselaer Polytechnic Institute

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Algorithms
  • Chains
  • Data Science
  • Equations
  • Estimators
  • Filters
  • Filtration
  • Image Restoration
  • Information Science
  • Kalman Filtering
  • Kalman Filters
  • Markov Chains
  • Probability
  • Statistics
  • Systems Engineering

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.

Technology Areas

  • Space
  • Space - Space Objects