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Adaptive constraint restoration and error analysis using a neural network

  • Computer Vision
  • Conference paper
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Advanced Topics in Artificial Intelligence (AI 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1342))

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Abstract

In this paper we present a restoration technique aimed at correcting image degradations by consideration of human visual criteria. A neural network model with an adaptive constraint factor is used. By considering local statistical information about regions within an image, the value of constraint factor can be selected which produces an optimal trade-off between noise suppression and edge preservation in each statistically homogeneous region. In addition a novel image error measure is presented which takes into account the statistical matching of homogeneous regions and its effect on human visual appraisal of image quality.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Sydney, 2006, Sydney, NSW, Australia

    Stuart W. Perry & Ling Guan

Authors
  1. Stuart W. Perry
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  2. Ling Guan
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Editor information

Abdul Sattar

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© 1997 Springer-Verlag Berlin Heidelberg

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Perry, S.W., Guan, L. (1997). Adaptive constraint restoration and error analysis using a neural network. In: Sattar, A. (eds) Advanced Topics in Artificial Intelligence. AI 1997. Lecture Notes in Computer Science, vol 1342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63797-4_61

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  • DOI: https://doi.org/10.1007/3-540-63797-4_61

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63797-4

  • Online ISBN: 978-3-540-69649-0

  • eBook Packages: Springer Book Archive

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