• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

An Optimized Multiple Fuzzy Membership Functions based Image Contrast Enhancement Technique

Vol. 12, No.3, March 31, 2018
10.3837/tiis.2018.03.013, Download Paper (Free):

Abstract

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.


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Cite this article

[IEEE Style]
Pushpa Mamoria and Deepa Raj, "An Optimized Multiple Fuzzy Membership Functions based Image Contrast Enhancement Technique," KSII Transactions on Internet and Information Systems, vol. 12, no. 3, pp. 1205-1223, 2018. DOI: 10.3837/tiis.2018.03.013

[ACM Style]
Mamoria, P. and Raj, D. 2018. An Optimized Multiple Fuzzy Membership Functions based Image Contrast Enhancement Technique. KSII Transactions on Internet and Information Systems, 12, 3, (2018), 1205-1223. DOI: 10.3837/tiis.2018.03.013