A processing which takes on an image or

A description of a two
dimensional light intensity function f(x,y), where the letters x and y
represents  spatial co-ordinates and the ‘f’
value at every point is directly relational to the gray level of the image at
that point is known to be an image 1. Digital image processing methods alters
the deficiency in an image into a modified improved version. From computer science,
image processing is some process of signal processing which takes on an image
or frames of videos as the input and outputs either an image or set of
parameters that is connected to the image 1. Lately, image enhancement has
become one of the central topics in image processing. Image enhancement includes
a collection of skills that are used to improve and develop an image visual appearance
– a figure description in fig 1- or a conversion of the image to an improved form
for suitability and understanding of human and machine interpretation.

 

Figure
1: An enhanced image
description

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Several kinds of image and
pictures are employed as the basis of information in recent communication
system and broad-based applications. Most images taken may have inherit some form
of degradation such as blurred image. Similarly, an image converted from
certain form to another such as scanning, transmitting, storing and a lot more
may also inherit some form of distortion. Mostly the degradation may occur at
the output and therefore the need to enhance the output image for an improved
visual appearance and appealing.

The chief goal of
enhancement of an image is to process the image such that the outcome serve the
purpose for which it was intended for a specific target or set application. In
most situations, the end effect of image enhancement can be recognized
visually. Although enhancement of image is very challenging issue in many
research and application areas, this aspect of image processing is a necessity
and needs expanded research to address certain technical challenges that occur
in daily operation. Image enhancement techniques is needed to  address or handle regular objects with regards
to image geometric transformations or recover certain characteristics by adjusting
the intensities or colors for the processing of medical images and other areas
of application of image processing as biometric image processing, satellite
image processing etc. There has not been any general theory of image
enhancement partly due to the fact that no universal standard for the quality
of an image has been proven. Once an image is taken for visual interpretation,
the eyewitness is the definitive judge of how well a specific approach works. Thus
leading to the development of varying classes of techniques over the past
decades 2 and 3.

A full proof implementation
of gray level image transformation is possible but a challenging task. In 2001,
to automatically find a measure for central tendency of an image brightness
histogram, 4 created an automatic contrast enhancement technique by shifting
and converting the histogram appropriately. A contrast enhancement algorithm based
on curvelet was proposed by 5 which tapped the properties of curvelet for
contrast stretching for effective enhancement of image contrast. This approach is
very efficient for correcting images which may contain noise. A cumulative
function was put forward by 6 to be used in together with histogram
equalization to realize contrast enhancements. In this paper an implementation
of gray level image enhancement techniques is undertaken. A software program
developed in Matlab is used to apply the techniques studied. The remaining of
the paper is organized as follows: In section 2 Gray level image transformation
techniques is introduced followed by section 3 with a brief explanation of the
application of Matlab and its implementation of the software. Section 4 details
the simulation experiment and results. Finally the paper concludes with Section
5.