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The automatic focus segmentation of multi-focus image fusion

Multi-focus image fusion is a method of increasing the image quality and preventing image redundancy. It is utilized in many fields such as medical diagnostic, surveillance, and remote sensing. There are various algorithms available nowadays. However, a common problem is still there, i.e. the method...

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Main Authors: Kamarul Hawari, Ghazali, Ismail, .
Format: Article
Language:English
Published: Polska Akademia Nauk 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/37776/1/The%20automatic%20focus%20segmentation%20of%20multi-focus%20image%20fusion_BPAST.pdf
http://umpir.ump.edu.my/id/eprint/37776/
https://doi.org/10.24425/bpasts.2022.140352
https://doi.org/10.24425/bpasts.2022.140352
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spelling my.ump.umpir.377762023-06-06T08:08:49Z http://umpir.ump.edu.my/id/eprint/37776/ The automatic focus segmentation of multi-focus image fusion Kamarul Hawari, Ghazali Ismail, . TK Electrical engineering. Electronics Nuclear engineering Multi-focus image fusion is a method of increasing the image quality and preventing image redundancy. It is utilized in many fields such as medical diagnostic, surveillance, and remote sensing. There are various algorithms available nowadays. However, a common problem is still there, i.e. the method is not sufficient to handle the ghost effect and unpredicted noises. Computational intelligence has developed quickly over recent decades, followed by the rapid development of multi-focus image fusion. The proposed method is multi-focus image fusion based on an automatic encoder-decoder algorithm. It uses deeplabV3+ architecture. During the training process, it uses a multi-focus dataset and ground truth. Then, the model of the network is constructed through the training process. This model was adopted in the testing process of sets to predict the focus map. The testing process is semantic focus processing. Lastly, the fusion process involves a focus map and multi-focus images to configure the fused image. The results show that the fused images do not contain any ghost effects or any unpredicted tiny objects. The assessment metric of the proposed method uses two aspects. The first is the accuracy of predicting a focus map, the second is an objective assessment of the fused image such as mutual information, SSIM, and PSNR indexes. They show a high score of precision and recall. In addition, the indexes of SSIM, PSNR, and mutual information are high. The proposed method also has more stable performance compared with other methods. Finally, the Resnet50 model algorithm in multi-focus image fusion can handle the ghost effect problem well. Polska Akademia Nauk 2022 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/37776/1/The%20automatic%20focus%20segmentation%20of%20multi-focus%20image%20fusion_BPAST.pdf Kamarul Hawari, Ghazali and Ismail, . (2022) The automatic focus segmentation of multi-focus image fusion. Bulletin of the Polish Academy of Sciences: Technical Sciences, 70 (1). pp. 1-8. ISSN 0239-7528 https://doi.org/10.24425/bpasts.2022.140352 https://doi.org/10.24425/bpasts.2022.140352
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Kamarul Hawari, Ghazali
Ismail, .
The automatic focus segmentation of multi-focus image fusion
description Multi-focus image fusion is a method of increasing the image quality and preventing image redundancy. It is utilized in many fields such as medical diagnostic, surveillance, and remote sensing. There are various algorithms available nowadays. However, a common problem is still there, i.e. the method is not sufficient to handle the ghost effect and unpredicted noises. Computational intelligence has developed quickly over recent decades, followed by the rapid development of multi-focus image fusion. The proposed method is multi-focus image fusion based on an automatic encoder-decoder algorithm. It uses deeplabV3+ architecture. During the training process, it uses a multi-focus dataset and ground truth. Then, the model of the network is constructed through the training process. This model was adopted in the testing process of sets to predict the focus map. The testing process is semantic focus processing. Lastly, the fusion process involves a focus map and multi-focus images to configure the fused image. The results show that the fused images do not contain any ghost effects or any unpredicted tiny objects. The assessment metric of the proposed method uses two aspects. The first is the accuracy of predicting a focus map, the second is an objective assessment of the fused image such as mutual information, SSIM, and PSNR indexes. They show a high score of precision and recall. In addition, the indexes of SSIM, PSNR, and mutual information are high. The proposed method also has more stable performance compared with other methods. Finally, the Resnet50 model algorithm in multi-focus image fusion can handle the ghost effect problem well.
format Article
author Kamarul Hawari, Ghazali
Ismail, .
author_facet Kamarul Hawari, Ghazali
Ismail, .
author_sort Kamarul Hawari, Ghazali
title The automatic focus segmentation of multi-focus image fusion
title_short The automatic focus segmentation of multi-focus image fusion
title_full The automatic focus segmentation of multi-focus image fusion
title_fullStr The automatic focus segmentation of multi-focus image fusion
title_full_unstemmed The automatic focus segmentation of multi-focus image fusion
title_sort automatic focus segmentation of multi-focus image fusion
publisher Polska Akademia Nauk
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/37776/1/The%20automatic%20focus%20segmentation%20of%20multi-focus%20image%20fusion_BPAST.pdf
http://umpir.ump.edu.my/id/eprint/37776/
https://doi.org/10.24425/bpasts.2022.140352
https://doi.org/10.24425/bpasts.2022.140352
_version_ 1768006890101407744
score 13.211869

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