Estimation and Detection of Images Degraded by Film-Grain Noise

Abstract

One goal of this study has been to use the theoretical and experimental results on film characteristics obtained by photographic scientists in order to define film-grain noise within the context of estimation theory. A detailed model for the overall photographic imaging system is presented. There are linear blurring effects at the initial and the final segments of this model to account for various optical and chemical degradations. The middle segment of the model represents signal dependence effects of film-grain noise and includes a nonlinear noise term. The accuracy of this model is tested by simulating images according to it and comparing the results to images of similar targets that were actually recorded on film. These comparisons point out that the model is a good representation of the photographic imaging system. The restoration of images degraded by film-grain noise is considered in two different contexts - estimation theory and detection theory. Under the topic of estimation, a discrete Wiener filter is developed which explicitly allows for the signal- dependence of the noise. The filter adaptively alters its characteristics based on nonstationary first order statistics of an image. This filter is shown to have an advantage over the conventional Wiener filter.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1976
Accession Number
ADA122438

Entities

People

  • Firouz Naderi

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Computers
  • Detectors
  • Digital Computers
  • Digital Images
  • Estimators
  • Image Processing
  • Image Restoration
  • Information Processing
  • Order Statistics
  • Photographic Film
  • Photographic Materials
  • Probability Density Functions
  • Random Variables
  • Statistical Analysis
  • Two Dimensional

Readers

  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.