EFFECTIVENESS OF PARTICLE SWARM OPTIMIZATION AS AN IMAGE ENHANCER: A COMPARATIVE STUDY
Abstract
Particle Swarm Optimization (PSO) algorithm represents a nature inspiredapproach for optimization problems. In this paper image enhancement is
considered as an optimization problem. Enhancement of images is mainly
done by maximizing the information content of the actual image. In the
present work a parameterized fitness function is used, which uses local and
global information of the images. An objective criterion for measuring image
enhancement is used which considers neighborhood and fitness data of the
images. Results are compared and analyzed with other enhancement
techniques like Histogram Equalization (HE), Linear Contrast Stretching (LCS)
and Genetic Algorithm (GA) based image Enhancements. Quality parameters
such as Root Mean Square error, Peak Signal to Noise Ratio has been
calculated along with Normalized Cross Correlation, Average Difference,
Structural content, Maximum Difference, Normalized Absolute Error to verify
the effectiveness of Particle Swarm Optimizations an image enhancement
technique.
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