Genetic algorithms for optimal image enhancement

https://doi.org/10.1016/0167-8655(94)90058-2Get rights and content

Abstract

Genetic algorithms represent a class of highly parallel adaptive search processes for solving a wide range of optimization and machine learning problems. The present work is an attempt to demonstrate their adaptivity and effectiveness for searching global optimal solutions in selecting an appropriate image enhancement operator automatically.

References (9)

There are more references available in the full text version of this article.

Cited by (111)

  • Contrast and brightness balance in image enhancement using Cuckoo Search-optimized image fusion

    2022, Journal of King Saud University - Computer and Information Sciences
    Citation Excerpt :

    Recent advancements in image enhancement have included the use of nature-inspired metaheuristic algorithms to automate it by optimizing the parameters of the algorithm by some defined objective function. ( Pal et al., 1994) proposed a Genetic Algorithm (GA)-based image enhancement with the combination of different transformation functions. ( Hashemi, 2010) applied an IE technique using a simple chromosome structure and genetic operators. (

  • Modeling and optimization of galena dissolution in a binary solution of nitric acid and ferric chloride using artificial neural network coupled with genetic algorithm and response surface methodology

    2020, South African Journal of Chemical Engineering
    Citation Excerpt :

    They combine survival of the fittest among string structures with an organized, yet randomized, data exchange to form a search strategy with some of the innovative flair of human search. They effectively take advantage of recorded data to conjecture on new search points with expected improved performance utilizing genetically inspired operators on potential solutions in an iterative way (Pal et al., 1994). A genetic algorithm is basically made up of three operators: reproduction, crossover and mutation.

  • Self-adaptive dragonfly based optimal thresholding for multilevel segmentation of digital images

    2018, Journal of King Saud University - Computer and Information Sciences
View all citing articles on Scopus
View full text