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DC Field | Value | Language |
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dc.contributor.author | Benchabaane, abderazzak | - |
dc.contributor.author | Moussaoui, Youcef | - |
dc.contributor.author | Bouafia, Riad | - |
dc.date.accessioned | 2024-02-07T09:46:44Z | - |
dc.date.available | 2024-02-07T09:46:44Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://dspace.univ-ouargla.dz/jspui/handle/123456789/35570 | - |
dc.description.abstract | Image fusion and deep learning are two prominent areas in the field of computer vision that have gained significant attention in recent years. Image fusion involves the combination of multiple images to create a single composite image with enhanced quality and improved perception .This study demonstrated the potential of deep learning techniques such as AlexNet , ResNet18 , VGG 19 , and SqueeseNet in image fusion, showing that the integration of deep learning models and fusion rules can improve quality, detail, and attributes. The results and discussions validate the effectiveness of the proposed method in terms of superior performance compared to traditional fusion techniques. | en_US |
dc.language.iso | en | en_US |
dc.publisher | UNIVERSITY KASDI MERBAH OUARGLA | en_US |
dc.subject | Image fusion | en_US |
dc.subject | deep learning | en_US |
dc.subject | AlexNet | en_US |
dc.subject | ResNet18 | en_US |
dc.subject | VGG 19 | en_US |
dc.subject | SqueeseNet | en_US |
dc.title | Image fusion using deep learning | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Département d'Electronique et des Télécommunications - Master |
Files in This Item:
File | Description | Size | Format | |
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MOUSSAOUI-BOUAFIA.pdf | 1,99 MB | Adobe PDF | View/Open |
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