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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/58548
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dc.contributor.authorYixiang Lu-
dc.contributor.authorWeijian Zhang-
dc.contributor.authorDawei Zhao-
dc.contributor.authorYucheng Qian-
dc.contributor.authorDavydau, M.-
dc.contributor.authorQingwei Gao-
dc.coverage.spatialNetherlandsen_US
dc.date.accessioned2024-12-23T12:13:17Z-
dc.date.available2024-12-23T12:13:17Z-
dc.date.issued2024-
dc.identifier.citationPTPFusion: A progressive infrared and visible image fusion network based on texture preserving / Yixiang Lu, Weijian Zhang, Dawei Zhao [et al.] // Image and Vision Computing. ‒ 2024. ‒ Vol. 151. ‒ P. 105287.en_US
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/58548-
dc.description.abstractInfrared and visible image fusion aims to provide a more comprehensive image for downstream tasks by highlighting the main target and maintaining rich texture information. Image fusion methods based on deep learning suffer from insufficient multimodal information extraction and texture loss. In this paper, we propose a texture-preserving progressive fusion network (PTPFusion) to extract complementary information from multimodal images to solve these issues. To reduce image texture loss, we design multiple consecutive texture-preserving blocks (TPB) to enhance fused texture. The TPB can enhance the features by using a parallel architecture consisting of a residual block and derivative operators. In addition, a novel cross-channel attention (CCA) fusion module is developed to obtaincomplementary information by modeling global feature interactions via cross-queries mechanism, followed by information fusion to highlight the feature of the salient target. To avoid information loss, the extracted features at different stages are merged as the output of TPB. Finally, the fused image will be generated by the decoder. Extensive experiments on three datasets show that our proposed fusion algorithm is better than existing state-of-the-art methods.en_US
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.subjectпубликации ученыхen_US
dc.subjectinfrared imagesen_US
dc.subjectprogressive fusion networken_US
dc.subjectcross-channel attentionen_US
dc.titlePTPFusion: A progressive infrared and visible image fusion network based on texture preservingen_US
dc.typeArticleen_US
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