Image recovery and segmentation using competitive learning in a layered network

Vir V. Phoha, William J.B. Oldham

Research output: Contribution to journalConference Articlepeer-review


In this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields (MRF), the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen's update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results compare favorably with recently published results using Markov Random Fields and mean field approximation.

Original languageEnglish (US)
Pages (from-to)74-86
Number of pages13
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Oct 29 1993
Externally publishedYes
EventNeural and Stochastic Methods in Image and Signal Processing II 1993 - San Diego, United States
Duration: Jul 11 1993Jul 16 1993

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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