  Image enhancement aims at improving the information content of original image
for a specific purpose. This purpose could be for visual interpretation or for
effective extraction of required details. Nevertheless, some acquired images
are often associated with pixels of low dynamic range and as such result in low
contrast images. Enhancing the contrast therefore tends to increase the dynamic
range of the gray levels in the acquired image so as to span the full intensity
range. Techniques such as Histogram Equalization (HE) and fuzzy technique can
be adopted for contrast enhancement. HE adjusts the contrast of an input image
by modifying the intensity distribution of its histogram. It is characterized
by providing a global approach to image enhancement, computationally fast and
easy to implement approach but can introduce unnatural artifacts and other
undesirable elements to the resulting image. Fuzzy technique on its part
enhances image by mapping the image gray level intensities into a fuzzy plane
using membership functions; modifying the membership functions as desired and
mapping back into the gray level plane. Thus, details at desired areas can be
enhanced at the expense of increase in computational cost. This paper explores
the effect of the use of HE and fuzzy technique to enhance low contrast images.
Their performances are evaluated using the Mean squared error (MSE), Peak to
signal noise ratio (PSNR), entropy and Absolute mean brightness error (AMBE).
