Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF)
When light travels through a water medium, selective attenuation and scattering have a profound impact on the underwater image. These limitations reduce image quality and impede the ability to perform visual tasks. The suggested integrated color correction with intelligence fusion of particle swarm...
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2023
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Online Access: | http://umpir.ump.edu.my/id/eprint/37981/1/Improving%20Images%20in%20Turbid%20Water%20through%20Enhanced%20Color%20Correction.pdf http://umpir.ump.edu.my/id/eprint/37981/ |
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my.ump.umpir.379812023-07-10T03:15:27Z http://umpir.ump.edu.my/id/eprint/37981/ Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF) Syafiq Qhushairy, Syamsul Amri Ahmad Shahrizan, Abdul Ghani Mohd Aiman Syahmi, Kamarul Baharin Mohd Yazid, Abu Fusaomi, Nagata TJ Mechanical engineering and machinery TS Manufactures When light travels through a water medium, selective attenuation and scattering have a profound impact on the underwater image. These limitations reduce image quality and impede the ability to perform visual tasks. The suggested integrated color correction with intelligence fusion of particle swarm technique (CCPF) is designed with four phases. The first phase presents a novel way to make improvement on underwater color cast. Limit the improvement to only red color channel. In the second phase, an image is then neutralized from the original image by brightness reconstruction technique resulting in enhancing the image contrast. Next, the mean adjustment based on particle swarm intelligence is implemented to improve the image detail. With the swarm intelligence method, the mean values of inferior color channels are shifted to be close to the mean value of a good color channel. Lastly, a fusion between the brightness reconstructed histogram and modified mean particle swarm intelligence histogram is applied to balance the image color. Analysis of underwater images taken in different depths shows that the proposed CCPF method improves the quality of the output image in terms of neutralizing effectiveness and details accuracy, consequently, significantly outperforming the other state-of-the-art methods. The proposed CCPF approach produces highest average entropy value of 7.823 and average UIQM value of 6.287. Penerbit UMP 2023-03 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/37981/1/Improving%20Images%20in%20Turbid%20Water%20through%20Enhanced%20Color%20Correction.pdf Syafiq Qhushairy, Syamsul Amri and Ahmad Shahrizan, Abdul Ghani and Mohd Aiman Syahmi, Kamarul Baharin and Mohd Yazid, Abu and Fusaomi, Nagata (2023) Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF). Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 5 (1). pp. 18-35. ISSN 2637-0883. (Published) ttps://doi.org/10.15282/mekatronika.v5i1.9085 ttps://doi.org/10.15282/mekatronika.v5i1.9085 |
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TJ Mechanical engineering and machinery TS Manufactures Syafiq Qhushairy, Syamsul Amri Ahmad Shahrizan, Abdul Ghani Mohd Aiman Syahmi, Kamarul Baharin Mohd Yazid, Abu Fusaomi, Nagata Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF) |
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When light travels through a water medium, selective attenuation and scattering have a profound impact on the underwater image. These limitations reduce image quality and impede the ability to perform visual tasks. The suggested integrated color correction with intelligence fusion of particle swarm technique (CCPF) is designed with four phases. The first phase presents a novel way to make improvement on underwater color cast. Limit the improvement to only red color channel. In the second phase, an image is then neutralized from the original image by brightness reconstruction technique resulting in enhancing the image contrast. Next, the mean adjustment based on particle swarm intelligence is implemented to improve the image detail. With the swarm intelligence method, the mean values of inferior color channels are shifted to be close to the mean value of a good color channel. Lastly, a fusion between the brightness reconstructed histogram and modified mean particle swarm intelligence histogram is applied to balance the image color. Analysis of underwater images taken in different depths shows that the proposed CCPF method improves the quality of the output image in terms of neutralizing effectiveness and details accuracy, consequently, significantly outperforming the other state-of-the-art methods. The proposed CCPF approach produces highest average entropy value of 7.823 and average UIQM value of 6.287. |
format |
Article |
author |
Syafiq Qhushairy, Syamsul Amri Ahmad Shahrizan, Abdul Ghani Mohd Aiman Syahmi, Kamarul Baharin Mohd Yazid, Abu Fusaomi, Nagata |
author_facet |
Syafiq Qhushairy, Syamsul Amri Ahmad Shahrizan, Abdul Ghani Mohd Aiman Syahmi, Kamarul Baharin Mohd Yazid, Abu Fusaomi, Nagata |
author_sort |
Syafiq Qhushairy, Syamsul Amri |
title |
Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF) |
title_short |
Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF) |
title_full |
Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF) |
title_fullStr |
Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF) |
title_full_unstemmed |
Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF) |
title_sort |
improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (ccpf) |
publisher |
Penerbit UMP |
publishDate |
2023 |
url |
http://umpir.ump.edu.my/id/eprint/37981/1/Improving%20Images%20in%20Turbid%20Water%20through%20Enhanced%20Color%20Correction.pdf http://umpir.ump.edu.my/id/eprint/37981/ |
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13.18916 |