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Erschienen in:
Moving Target Detection Under Turbulence Degraded Visible and Infrared Image Sequences Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Image Contrast Enhancement Using Hybrid Elitist Ant System, Elitism-Based Immigrants Genetic Algorithm and Simulated Annealing

verfasst von : Rajeev Kumar, Anand Gupta, Apoorv Gupta, Aman Bansal

Erschienen in: Proceedings of 2nd International Conference on Computer Vision & Image Processing

Verlag: Springer Singapore

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Abstract

Contrast enhancement is a technique which is used to expand the range of intensities within the image to make its features more distinct and easily perceptible to the human eye. It has found many applications ranging from medical to satellite imagery where the primary aim is to find hidden or minute details within an image. Through literary research, the authors have realised that the existing approaches lag behind in enhancing the contrast of an image. Hence in the present paper, an improved contrast enhancement technique is proposed which is based on the hybrid combination of nature-based metaheuristics: Elitist Ant System (EAS), Elitism-based Genetic Algorithm (EIGA) and Simulated Annealing (SA). EAS and EIGA work together to search globally for the optimum solution which is then refined by SA locally. Through experiment, it is observed that the proposed algorithm is efficiently improving the contrast of an image when compared with existing algorithms.

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Vorheriges Kapitel Stochastic Assimilation Technique for Cloud Motion Analysis
Nächstes Kapitel A Novel Robust Reversible Watermarking Technique Based on Prediction Error Expansion for Medical Images
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Metadaten
Titel
Image Contrast Enhancement Using Hybrid Elitist Ant System, Elitism-Based Immigrants Genetic Algorithm and Simulated Annealing
verfasst von
Rajeev Kumar
Anand Gupta
Apoorv Gupta
Aman Bansal
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Proceedings of 2nd International Conference on Computer Vision & Image Processing

Print ISBN: 978-981-10-7894-1

Electronic ISBN: 978-981-10-7895-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-7895-8_10

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