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Genetic algorithms (GAs) represent a class of highly parallel
adaptive search processes for solving a wide range of
optimization and machine learning problems. In this paper, a new
technique for contrast enhancement is proposed, which developed
from the method of Pal with its modifications. The suggested
method uses GA to search an appropriate gray-level
transformation function automatically for image contrast
enhancement. To make the algorithm context sensitive, we
consider the relationships between a pixel and the pixels
surrounding it as the fitness function of GA. Finally, we use
three tools to measure the performance of contrast-enhancing
methods. The three measurements are image contrast value, image
information loss value, and local intensity variance value. The
goal is to increase an image''s contrast value while keeping both
the information loss value and the local intensity variance
value low. In the experiments, we have compared the performance
of the suggested method with that of the ordinary histogram
equalization technique and the method of Pal. The results were
then evaluated by the three tools. The suggested method
performed well both analytically and visually.
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