A Nonlinear Filtering Technique for Digitized Images Degraded by Film-Grain Noise,

Abstract

A class of nonlinear recursive filtering and smoothing algorithms is investigated for use in digital processing of images degraded by film-grain noise. These algorithms are developed by using a Bayesian perturbation analysis and a class of 'almost Gaussian' conditional probability distributions to obtain results that are formally accurate to first order in the perturbation parameters. The nonlinear features of the resulting algorithms are used to account for the signal dependence of film-grain noise and the nonlinear relation between optical density and intensity. These algorithms are tested on a photographic film negative. Some improvement over conventional Wiener filtering is found. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 30, 1978
Accession Number
ADA063134

Entities

People

  • Warren W. William

Organizations

  • United States Naval Research Laboratory

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Bayes Filters
  • Digital Image Processing
  • Filters
  • Filtration
  • Gaussian Distributions
  • Image Processing
  • Linear Filtering
  • Markov Processes
  • Nonlinear Dynamics
  • Photographic Grain
  • Photographic Images
  • Probability
  • Probability Distributions
  • Random Variables
  • Two Dimensional
  • Weighting Functions

Readers

  • Calculus or Mathematical Analysis
  • Phased Array Antenna Design.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms