INSTITUTIONAL DIGITAL REPOSITORY

A feature level image fusion for Night-Vision context enhancement using Arithmetic optimization algorithm based image segmentation

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dc.contributor.author Mittal, N.
dc.contributor.author Singh, S.
dc.contributor.author Singh, H.
dc.contributor.author Hussien, A.G.
dc.contributor.author Sroubek, F.
dc.date.accessioned 2022-08-15T09:43:50Z
dc.date.available 2022-08-15T09:43:50Z
dc.date.issued 2022-08-15
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/3802
dc.description.abstract Images are fused to produce a composite image by combining key characteristics of the source images in image fusion. It makes the fused image better for human vision and machine vision. A novel procedure of Infrared (IR) and Visible (Vis) image fusion is proposed in this manuscript. The main challenges of feature level image fusion are that it will introduce artifacts and noise in the fused image. To preserve the meaningful information without adding artifacts from the source input images, weight map computed from Arithmetic optimization algorithm (AOA) is used for the image fusion process. In this manuscript, feature level fusion is performed after refining the weight maps using a weighted least square optimization (WLS) technique. Through this, the derived salient object details are merged into the visual image without introducing distortion. To affirm the validity of the proposed methodology simulation results are carried for twenty-one image data sets. It is concluded from the qualitative and quantitative experimental analysis that the proposed method works well for most of the image data sets and shows better performance than certain traditional existing models. en_US
dc.language.iso en_US en_US
dc.subject AOA en_US
dc.subject Image Fusion en_US
dc.subject Image segmentation en_US
dc.subject Infrared (IR) en_US
dc.subject visible (Vis) image en_US
dc.subject WLS en_US
dc.title A feature level image fusion for Night-Vision context enhancement using Arithmetic optimization algorithm based image segmentation en_US
dc.type Article en_US


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