Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3961
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dc.contributor.authorBanerjee, Ankan-
dc.contributor.authorPatra, Dipti-
dc.contributor.authorRoy, Pradipta-
dc.date.accessioned2023-03-02T06:51:10Z-
dc.date.available2023-03-02T06:51:10Z-
dc.date.issued2023-02-
dc.identifier.citation3rd IEEE International Conference On Range Technology(ICORT 2023), Chandipur, India, 23 To 25 February 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/3961-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractImage fusion of the infrared (IR) and visible images has become one of the essential pre-processing steps for image analysis in medical, defense, remote sensing, etc. Till now, the spatial and transform domain methods have produced fused images that are not of good quality. Also, deep learning-based techniques are mostly concerned with feature extraction from the source images before fusion, but the distinct features of the source images have been overlooked. In the proposed model an attempt has been made to extract the edges of the IR image followed by the feature extraction of the IR and visible images using a densenetwork based convolutional neural network. The feature maps from the two pipelines are concatenated along with the edgeextracted IR image and the enhanced visible image to obtain the final fused image. The proposed model has been tested on the TNO image dataset. The quantitative and qualitative results show that the proposed method outperformed when compared with the state-of-the-art method in the entropy content and also produces fused images with better human perception qualityen_US
dc.subjectImage fusionen_US
dc.subjectDeep learningen_US
dc.subjectDense networken_US
dc.subjectInfrared imagesen_US
dc.subjectVisible imagesen_US
dc.subjectVisual perceptionen_US
dc.titleA Dense Network Based Framework for the Fusion of Infrared and Visible Images with Edge Extractionen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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