Comparative analysis of evolutionary algorithms for image enhancement

by   Anupriya Gogna, et al.

Evolutionary algorithms are metaheuristic techniques that derive inspiration from the natural process of evolution. They can efficiently solve (generate acceptable quality of solution in reasonable time) complex optimization (NP-Hard) problems. In this paper, automatic image enhancement is considered as an optimization problem and three evolutionary algorithms (Genetic Algorithm, Differential Evolution and Self Organizing Migration Algorithm) are employed to search for an optimum solution. They are used to find an optimum parameter set for an image enhancement transfer function. The aim is to maximize a fitness criterion which is a measure of image contrast and the visibility of details in the enhanced image. The enhancement results obtained using all three evolutionary algorithms are compared amongst themselves and also with the output of histogram equalization method.



There are no comments yet.


page 12

page 13

page 14

page 15


Analysis of Evolutionary Algorithms on Fitness Function with Time-linkage Property

In real-world applications, many optimization problems have the time-lin...

Multiobjective Optimization Differential Evolution Enhanced with Principle Component Analysis for Constrained Optimization

Multiobjective optimization evolutionary algorithms have been successful...

Influence of the Binomial Crossover on Performance of Evolutionary Algorithms

In differential Evolution (DE) algorithms, a crossover operation filteri...

Subpopulation Diversity Based Selecting Migration Moment in Distributed Evolutionary Algorithms

In distributed evolutionary algorithms, migration interval is used to de...

A multiset model of multi-species evolution to solve big deceptive problems

This chapter presents SMuGA, an integration of symbiogenesis with the Mu...

ISEA: Image Steganalysis using Evolutionary Algorithms

NP-hard problems always have been attracting scientists' attentions, and...

On the performance of a hybrid genetic algorithm in dynamic environments

The ability to track the optimum of dynamic environments is important in...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.