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dc.contributor.authorBenchabaane, abderazzak-
dc.contributor.authorMoussaoui, Youcef-
dc.contributor.authorBouafia, Riad-
dc.date.accessioned2024-02-07T09:46:44Z-
dc.date.available2024-02-07T09:46:44Z-
dc.date.issued2023-
dc.identifier.urihttps://dspace.univ-ouargla.dz/jspui/handle/123456789/35570-
dc.description.abstractImage 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.isoenen_US
dc.publisherUNIVERSITY KASDI MERBAH OUARGLAen_US
dc.subjectImage fusionen_US
dc.subjectdeep learningen_US
dc.subjectAlexNeten_US
dc.subjectResNet18en_US
dc.subjectVGG 19en_US
dc.subjectSqueeseNeten_US
dc.titleImage fusion using deep learningen_US
dc.typeThesisen_US
Appears in Collections:Département d'Electronique et des Télécommunications - Master

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