  Image enhancement plays an important role in several application in the field
of computer vision and image processing. Histogram specification (HS) is one of
the most widely used techniques for contrast enhancement of an image, which
requires an appropriate probability density function for the transformation. In
this paper, we propose a fuzzy method to find a suitable PDF automatically for
histogram specification using interval type - 2 (IT2) fuzzy approach, based on
the fuzzy membership values obtained from the histogram of input image. The
proposed algorithm works in 5 stages which includes - symmetric Gaussian
fitting 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 to
find membership values - point-wise method, center of weight method, area
method, and karnik-mendel (KM) method. The framework is sensitive to local
variations in the histogram and chooses the best PDF so as to improve contrast
enhancement. Experimental validity of the methods used is illustrated by
qualitative and quantitative analysis on several images using the image quality
index - Average Information Content (AIC) or Entropy, and by comparison with
the commonly used algorithms such as Histogram Equalization (HE), Recursive
Mean-Separate Histogram Equalization (RMSHE) and Brightness Preserving Fuzzy
Histogram Equalization (BPFHE). It has been found out that on an average, our
algorithm improves the AIC index by 11.5% as compared to the index obtained by
histogram equalisation.
