Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer
Image contrast enhancement is a very important phase for processing of digital images. The main goal of image contrast enhancement is to improve the visual quality by improving the contrast level of images which were distorted or degraded due to casual acquisition of images. The most popular method...
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doaj-f00f5118dedd41f0b4d0e7c135a0fa502021-03-30T03:46:58ZengIEEEIEEE Access2169-35362020-01-01816919616921410.1109/ACCESS.2020.30240959195884Gray Level Image Contrast Enhancement Using Barnacles Mating OptimizerShameem Ahmed0https://orcid.org/0000-0003-1795-3361Kushal Kanti Ghosh1Suman Kumar Bera2https://orcid.org/0000-0001-6968-2079Friedhelm Schwenker3https://orcid.org/0000-0001-5118-0812Ram Sarkar4https://orcid.org/0000-0001-8813-4086Department of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaDepartment of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaDepartment of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaInstitute of Neural Information Processing, Ulm University, Ulm, GermanyDepartment of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaImage contrast enhancement is a very important phase for processing of digital images. The main goal of image contrast enhancement is to improve the visual quality by improving the contrast level of images which were distorted or degraded due to casual acquisition of images. The most popular method to perform this task is Histogram Equalization (HE). However, the exhaustive approach taken during HE is an algorithmically complex task. In this paper, we have considered image contrast enhancement as an optimization problem, where a new meta-heuristic algorithm, called Barnacles Mating Optimizer (BMO) is used to find the optimal solution for this optimization problem. A grey level mapping technique is used here to convert an image to a solution of the optimization problem. The algorithm has been evaluated on five publicly available datasets: Kodak, MIT-Adobe FiveK images, H-DIBCO 2016, and H-DIBCO 2018. It is also applied on some standard images like Boy, Lena, Lifting body and Zebra. The obtained results clearly display the effectiveness of the proposed method. The results obtained on the Kodak images are compared with many state-of-the-art methods present in the literature, and the comparison proves the superiority of the proposed method. To test the applicability of BMO in solving real world problems, we have applied it as a pre-processing step in binarization of H-DIBCO 2016 and H-DIBCO 2018 datasets. The source code of this work is available at https://github.com/ahmed-shameem/Projects.https://ieeexplore.ieee.org/document/9195884/Barnacle Mating Optimizerimage contrast enhancementmeta-heuristicevolutionary algorithmDIBCO |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shameem Ahmed Kushal Kanti Ghosh Suman Kumar Bera Friedhelm Schwenker Ram Sarkar |
spellingShingle |
Shameem Ahmed Kushal Kanti Ghosh Suman Kumar Bera Friedhelm Schwenker Ram Sarkar Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer IEEE Access Barnacle Mating Optimizer image contrast enhancement meta-heuristic evolutionary algorithm DIBCO |
author_facet |
Shameem Ahmed Kushal Kanti Ghosh Suman Kumar Bera Friedhelm Schwenker Ram Sarkar |
author_sort |
Shameem Ahmed |
title |
Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer |
title_short |
Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer |
title_full |
Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer |
title_fullStr |
Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer |
title_full_unstemmed |
Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer |
title_sort |
gray level image contrast enhancement using barnacles mating optimizer |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Image contrast enhancement is a very important phase for processing of digital images. The main goal of image contrast enhancement is to improve the visual quality by improving the contrast level of images which were distorted or degraded due to casual acquisition of images. The most popular method to perform this task is Histogram Equalization (HE). However, the exhaustive approach taken during HE is an algorithmically complex task. In this paper, we have considered image contrast enhancement as an optimization problem, where a new meta-heuristic algorithm, called Barnacles Mating Optimizer (BMO) is used to find the optimal solution for this optimization problem. A grey level mapping technique is used here to convert an image to a solution of the optimization problem. The algorithm has been evaluated on five publicly available datasets: Kodak, MIT-Adobe FiveK images, H-DIBCO 2016, and H-DIBCO 2018. It is also applied on some standard images like Boy, Lena, Lifting body and Zebra. The obtained results clearly display the effectiveness of the proposed method. The results obtained on the Kodak images are compared with many state-of-the-art methods present in the literature, and the comparison proves the superiority of the proposed method. To test the applicability of BMO in solving real world problems, we have applied it as a pre-processing step in binarization of H-DIBCO 2016 and H-DIBCO 2018 datasets. The source code of this work is available at https://github.com/ahmed-shameem/Projects. |
topic |
Barnacle Mating Optimizer image contrast enhancement meta-heuristic evolutionary algorithm DIBCO |
url |
https://ieeexplore.ieee.org/document/9195884/ |
work_keys_str_mv |
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