Digital Image Processing by Use of Local Statistics.

Abstract

Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays were developed, based on their local mean and variance. These algorithms are nonrecursive and do not require a transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where parallel processors can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square-error estimator in its simplest form is applied to obtain the noise-filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 x 256 pixels are given. Results show that in most cases the techniques developed in this report are readily adaptable to real-time image processing. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1978
Accession Number
ADA053843

Entities

People

  • Jong-Sen Lee

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Digital Image Processing
  • Digital Images
  • Filters
  • Filtration
  • Frequency Domain
  • Image Processing
  • Image Restoration
  • Information Processing
  • Kalman Filtering
  • Mathematical Filters
  • Military Research
  • Noise
  • Parallel Processing
  • Signal Processing
  • Statistics
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.
  • Computer Vision.