A Downhill Simplex Algorithm for Estimating Morphological Degradation Model Parameters

Abstract

Noise models are crucial for designing image restoration algorithms, generating synthetic training data, and predicting algorithm performance. However, to accomplish any of these tasks, an estimate of the degradation model parameters is essential. In this paper we describe a parameter estimation algorithm for a morphological, binary image degradation model. The inputs to the estimation algorithm are i) the degraded image, and ii) information regarding the font type (italic, bold, serif, sans serif). We simulate degraded images and search for the optimal parameter by looking for a parameter value for which the neighborhood pattern distributions in the simulated image and the given degraded image are most similar. The parameter space is searched using the Nelder-Mead downhill simplex algorithm. We use the p-value of the kolmogorov-Smirnov test for the measure of similarity between the two neighborhood pattern distributions. We show results of our algorithm on degraded document images.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2001
Accession Number
ADA458745

Entities

People

  • Qigong Zheng
  • Tapas Kanungo

Organizations

  • University of Maryland

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Degradation
  • Histograms
  • Image Restoration
  • Information Operations
  • Instructions
  • Language
  • Mathematics
  • Simplex Method
  • Universities

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Computer Vision.
  • Operations Research

Technology Areas

  • Space
  • Space - Space Objects