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Interactive image enhancement by fuzzy relaxation

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Abstract

In this paper, an interactive image enhancement (IIE) technique based on fuzzy relaxation is presented, which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications. First, based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement, an improved version of this algorithm, which is called FuzzIIE Method 1, is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm. Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function, in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.

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Authors and Affiliations

  1. School of Computing, De Montfort University, Leicester, LE1 9BH, UK

    Shang-Ming Zhou & Robert John

  2. Department of Computer Science, University of Essex, Colchester, CO3 4SQ, UK

    John Q. Gan

  3. Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk, VA, 23529-0218, USA

    Li-Da Xu

Authors
  1. Shang-Ming Zhou
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  2. John Q. Gan
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  3. Li-Da Xu
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  4. Robert John
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Corresponding author

Correspondence to Shang-Ming Zhou.

Additional information

Shang-Ming Zhou is a research fellow with Centre for Computational Intelligence, School of Computing, De Montfort University in the UK. His research interests include fuzzy logic systems (type-1 and type-2) and applications, decision support systems under uncertainty environments, model interpretability and transparency in data-driven neurofuzzy systems, kernel machine learning (inc support vector machines) with fuzzy computing, artificial neural networks and pattern recognition, intelligent signal and image processing. He has published extensively in these areas.

John Q. Gan received the B.Sc. degree in electronic engineering from Northwestern Polytechnic University, China, in 1982, the M.Eng. degree in automatic control and the Ph.D. degree in biomedical electronics from Southeast University, China, in 1985 and 1991, respectively. He is a reader in Computer Science at the University of Essex, UK.

Dr. Gan has co-authored a book and published over 100 research papers. His research interests include neurofuzzy computation and machine intelligence, pattern recognition, signal processing, data fusion, brain-computer interfaces, robotics, and intelligent systems.

Li-Da Xu is a professor of Information Technology at Old Dominion University, USA. He serves as the Chair of IFIP TC8.9 and IEEE SMC Technical Committee on Enterprise Information Systems. He is the founding Editor-in-Chief of the journal Enterprise Information Systems, Associate Editor of IEEE Transactions SMC — Part C and IEEE Transactions on Industrial Informatics. He also is the regional editor of Expert Systems, and Editorial Board member of Systems Research and Behavioral Science.

Dr. Xu is the author of more than 100 papers. His research interests include enterprise information systems, intelligent system, and decision support systems.

Robert John received the B.Sc. (Hons.) degree in mathematics from Leicester Polytechnic, Leicester, U.K., the M.Sc. degree in statistics from UMIST, Manchester, U.K., and the Ph.D. degree in type-2 fuzzy logic De Montfort University, Leicester, U.K., in 1979, 1981, and 2000, respectively. Currently, he is a professor and the director of the Centre for Computational Intelligence at De Montfort University.

Prof. John is a Member of the Editorial Board of International Journal of Cognitive Neurodynamics, International Journal of Computational Intelligence, International Journal for Computational Intelligence and Information and Systems Sciences. He is a vice-chairman of the Fuzzy Technical Committee (FTC) of the IEEE Neural Networks, and a Member of EPSRC College in the UK. He is a co-general chair of the FUZZ-IEEE conference in London in 24–26 July, 2007. He has published extensively in the area of type-2 fuzzy logic and his research interests include the general field of modelling human decision making using type-2 fuzzy logic.

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Zhou, SM., Gan, J.Q., Xu, LD. et al. Interactive image enhancement by fuzzy relaxation. Int J Automat Comput 4, 229–235 (2007). https://doi.org/10.1007/s11633-007-0229-7

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  • DOI: https://doi.org/10.1007/s11633-007-0229-7

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