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Image enhancement plays an important role in several application in the fieldof computer vision and image processing. Histogram specification (HS) is one ofthe most widely used techniques for contrast enhancement of an image, whichrequires an appropriate probability density function for the transformation. Inthis paper, we propose a fuzzy method to find a suitable PDF automatically forhistogram specification using interval type - 2 (IT2) fuzzy approach, based onthe fuzzy membership values obtained from the histogram of input image. Theproposed algorithm works in 5 stages which includes - symmetric Gaussianfitting on the histogram, extraction of IT2 fuzzy membership functions (MFs)and therefore, footprint of uncertainty (FOU), obtaining membership value (MV),generating PDF and application of HS. We have proposed 4 different methods tofind membership values - point-wise method, center of weight method, areamethod, and karnik-mendel (KM) method. The framework is sensitive to localvariations in the histogram and chooses the best PDF so as to improve contrastenhancement. Experimental validity of the methods used is illustrated byqualitative and quantitative analysis on several images using the image qualityindex - Average Information Content (AIC) or Entropy, and by comparison withthe commonly used algorithms such as Histogram Equalization (HE), RecursiveMean-Separate Histogram Equalization (RMSHE) and Brightness Preserving FuzzyHistogram Equalization (BPFHE). It has been found out that on an average, ouralgorithm improves the AIC index by 11.5% as compared to the index obtained byhistogram equalisation.

An Interval Type-2 Fuzzy Approach to Automatic PDF Generation for Histogram Specification