Image restoration using modified hopfield fuzzy regularization method

Mohsin Bilal, Muhammad Sharif, M. Arfan Jaffar, Ayyaz Hussain, Anwar M. Mirza

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

This paper addresses one of the primary problems of visual information processing known as image restoration. Image restoration is a challenging task because of its ill-posed inverse nature. A modified Hopfield neural network with fuzzy adaptive regularization is proposed that shows potential to minimize constraint mean square error in order to guarantee the optimized results. Adaptive regularization was achieved by using fuzzy quasi-range edge detector. The visual results along with the statistical measurements of the resultant images are presented in the paper. Improved SNRs show that the fuzzy regularization method is superior to other statistical and neural network methods when used along with the modified Hopfield neural network.

Original languageEnglish
Title of host publication2010 5th International Conference on Future Information Technology, FutureTech 2010 - Proceedings
DOIs
StatePublished - 2010
Externally publishedYes
Event5th International Conference on Future Information Technology, FutureTech 2010 - Busan, Korea, Republic of
Duration: 20 May 201024 May 2010

Publication series

Name2010 5th International Conference on Future Information Technology, FutureTech 2010 - Proceedings

Conference

Conference5th International Conference on Future Information Technology, FutureTech 2010
Country/TerritoryKorea, Republic of
CityBusan
Period20/05/1024/05/10

Keywords

  • Constraint mean square error
  • Edge detection
  • Fuzzy regularization
  • Image restoration
  • Modified hopfield neural network
  • Neural networks
  • Space variant/invariant distortions

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