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Multi-Thresholded Histogram Equalization Based on Parameterless Artificial Bee Colony

Multi-Thresholded Histogram Equalization Based on Parameterless Artificial Bee Colony
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Author(s): Krishna Gopal Dhal (Midnapore College (Autonomous), India), Mandira Sen (Tata Consultancy Services, India), Swarnajit Ray (JBMatrix Technology Pvt. Ltd., India) and Sanjoy Das (University of Kalyani, India)
Copyright: 2018
Pages: 19
Source title: Incorporating Nature-Inspired Paradigms in Computational Applications
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-5020-4.ch004

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Abstract

This chapter presents a novel variant of histogram equalization (HE) method called multi-thresholded histogram equalization (MTHE), depending on entropy-based multi-level thresholding-based segmentation. It is reported that proper segmentation of the histogram significantly assists the HE variants to maintain the original brightness of the image, which is one of the main criterion of the consumer electronics field. Multi-separation-based HE variants are also very effective for multi-modal histogram-based images. But, proper multi-seaparation of the histogram increases the computational time of the corresponding HE variants. In order to overcome that problem, one novel parameterless artifical bee colony (ABC) algorithm is employed to solve the multi-level thresholding problem. Experimental results prove that proposed parameterless ABC helps to reduce the computational time significantly and the proposed MTHE outperforms several existing HE varints in brightness preserving histopathological image enhancement domain.

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