Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm
Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image de...
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ftdoajarticles:oai:doaj.org/article:f3477637350c4852900113fc2bdd7878 2023-09-26T15:21:45+02:00 Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm Rusul Sabah Jebur Mohd Hazli Bin Mohamed Zabil Dalal Abdulmohsin Hammood Lim Kok Cheng Ali Al-Naji 2023-08-01T00:00:00Z https://doi.org/10.3390/technologies11040111 https://doaj.org/article/f3477637350c4852900113fc2bdd7878 EN eng MDPI AG https://www.mdpi.com/2227-7080/11/4/111 https://doaj.org/toc/2227-7080 doi:10.3390/technologies11040111 2227-7080 https://doaj.org/article/f3477637350c4852900113fc2bdd7878 Technologies, Vol 11, Iss 111, p 111 (2023) image denoising deep learning Bi-LSTM CNN SI-OPA Technology T article 2023 ftdoajarticles https://doi.org/10.3390/technologies11040111 2023-08-27T00:34:43Z Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications. Article in Journal/Newspaper Orca Directory of Open Access Journals: DOAJ Articles Technologies 11 4 111 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
topic |
image denoising deep learning Bi-LSTM CNN SI-OPA Technology T |
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image denoising deep learning Bi-LSTM CNN SI-OPA Technology T Rusul Sabah Jebur Mohd Hazli Bin Mohamed Zabil Dalal Abdulmohsin Hammood Lim Kok Cheng Ali Al-Naji Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm |
topic_facet |
image denoising deep learning Bi-LSTM CNN SI-OPA Technology T |
description |
Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications. |
format |
Article in Journal/Newspaper |
author |
Rusul Sabah Jebur Mohd Hazli Bin Mohamed Zabil Dalal Abdulmohsin Hammood Lim Kok Cheng Ali Al-Naji |
author_facet |
Rusul Sabah Jebur Mohd Hazli Bin Mohamed Zabil Dalal Abdulmohsin Hammood Lim Kok Cheng Ali Al-Naji |
author_sort |
Rusul Sabah Jebur |
title |
Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm |
title_short |
Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm |
title_full |
Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm |
title_fullStr |
Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm |
title_full_unstemmed |
Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm |
title_sort |
image denoising using hybrid deep learning approach and self-improved orca predation algorithm |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/technologies11040111 https://doaj.org/article/f3477637350c4852900113fc2bdd7878 |
genre |
Orca |
genre_facet |
Orca |
op_source |
Technologies, Vol 11, Iss 111, p 111 (2023) |
op_relation |
https://www.mdpi.com/2227-7080/11/4/111 https://doaj.org/toc/2227-7080 doi:10.3390/technologies11040111 2227-7080 https://doaj.org/article/f3477637350c4852900113fc2bdd7878 |
op_doi |
https://doi.org/10.3390/technologies11040111 |
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Technologies |
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111 |
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