{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T15:57:36Z","timestamp":1791907056900,"version":"4.3.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T00:00:00Z","timestamp":1768694400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62462043, 62067006"],"award-info":[{"award-number":["62462043, 62067006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key R&D Program of Gansu Province","award":["25YFGA047"],"award-info":[{"award-number":["25YFGA047"]}]},{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"crossref","award":["23JRRA847, 21JR7RA300"],"award-info":[{"award-number":["23JRRA847, 21JR7RA300"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-35763-4","type":"journal-article","created":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T18:34:29Z","timestamp":1768761269000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Infrared and visible image fusion via visual enhancement and semantic coupling"],"prefix":"10.1038","volume":"16","author":[{"given":"Yanchun","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,18]]},"reference":[{"key":"35763_CR1","doi-asserted-by":"publisher","first-page":"10535","DOI":"10.1109\/TPAMI.2023.3261282","volume":"45","author":"X Zhang","year":"2023","unstructured":"Zhang, X. & Demiris, Y. Visible and infrared image fusion using deep learning. IEEE Trans. Pattern Anal. Mach. Intell 45, 10535\u201310554 (2023).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"35763_CR2","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","volume":"45","author":"J Ma","year":"2019","unstructured":"Ma, J., Ma, Y. & Li, C. Infrared and visible image fusion methods and applications: A survey. Inf. fusion 45, 153\u2013178 (2019).","journal-title":"Inf. fusion"},{"key":"35763_CR3","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","volume":"76","author":"H Zhang","year":"2021","unstructured":"Zhang, H. et al. Image fusion meets deep learning: A survey and perspective. Inf. fusion 76, 323\u2013336 (2021).","journal-title":"Inf. fusion"},{"key":"35763_CR4","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1007\/s11263-018-1117-z","volume":"127","author":"J Ma","year":"2019","unstructured":"Ma, J. et al. Locality preserving matching. Int. J. Comput. Vis. 127, 512\u2013531 (2019).","journal-title":"Int. J. Comput. Vis."},{"key":"35763_CR5","first-page":"1","volume":"61","author":"B Yang","year":"2023","unstructured":"Yang, B. et al. From trained to untrained: A novel change detection framework using randomly initialized models with spatial-channel augmentation for hyperspectral images. IEEE Transactions on Geosci. Remote. Sens. 61, 1\u201314 (2023).","journal-title":"IEEE Transactions on Geosci. Remote. Sens."},{"key":"35763_CR6","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A. & Liao, H.-Y. M. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 7464\u20137475 (2023).","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"35763_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.2986875","volume":"70","author":"Y Yang","year":"2020","unstructured":"Yang, Y. et al. Infrared and visible image fusion using visual saliency sparse representation and detail injection model. IEEE Trans. Instrum. Meas. 70, 1\u201315 (2020).","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"35763_CR8","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1109\/TMM.2021.3057493","volume":"24","author":"J Chen","year":"2021","unstructured":"Chen, J., Li, X., Luo, L. & Ma, J. Multi-focus image fusion based on multi-scale gradients and image matting. IEEE Trans. Multimedia 24, 655\u2013667 (2021).","journal-title":"IEEE Trans. Multimedia"},{"key":"35763_CR9","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ins.2019.08.066","volume":"508","author":"J Chen","year":"2020","unstructured":"Chen, J. et al. Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Inf. Sci. 508, 64\u201378 (2020).","journal-title":"Inf. Sci."},{"key":"35763_CR10","doi-asserted-by":"publisher","first-page":"3658","DOI":"10.1109\/TGRS.2014.2381272","volume":"53","author":"Q Wei","year":"2015","unstructured":"Wei, Q., Bioucas-Dias, J., Dobigeon, N. & Tourneret, J.-Y. Hyperspectral and multispectral image fusion based on a sparse representation. IEEE Trans. Geosci. Remote Sens. 53, 3658\u20133668 (2015).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"35763_CR11","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.infrared.2017.04.018","volume":"83","author":"C Liu","year":"2017","unstructured":"Liu, C., Qi, Y. & Ding, W. Infrared and visible image fusion method based on saliency detection in sparse domain. Infrared Phys. & Technol. 83, 94\u2013102 (2017).","journal-title":"Infrared Phys. & Technol."},{"key":"35763_CR12","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.infrared.2018.01.003","volume":"89","author":"J Li","year":"2018","unstructured":"Li, J., Song, M. & Peng, Y. Infrared and visible image fusion based on robust principal component analysis and compressed sensing. Infrared Phys. & Technol. 89, 129\u2013139 (2018).","journal-title":"Infrared Phys. & Technol."},{"key":"35763_CR13","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","volume":"54","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y. et al. IFCNN: A general image fusion framework based on convolutional neural network. Inf. Fusion 54, 99\u2013118 (2020).","journal-title":"Inf. Fusion"},{"key":"35763_CR14","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2018","unstructured":"Li, H. & Wu, X.-J. DenseFuse: A fusion approach to infrared and visible images. IEEE Trans. Image Process 28, 2614\u20132623 (2018).","journal-title":"IEEE Trans. Image Process"},{"key":"35763_CR15","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.inffus.2021.02.023","volume":"73","author":"H Li","year":"2021","unstructured":"Li, H., Wu, X.-J. & Kittler, J. RFN-Nest: An end-to-end residual fusion network for infrared and visible images. Inf. Fusion 73, 72\u201386 (2021).","journal-title":"Inf. Fusion"},{"key":"35763_CR16","first-page":"1134","volume":"7","author":"H Zhang","year":"2021","unstructured":"Zhang, H., Yuan, J., Tian, X. & Ma, J. GAN-FM: Infrared and visible image fusion using GAN with full-scale skip connection and dual Markovian discriminators. IEEE Trans. Comput. Imag 7, 1134\u20131147 (2021).","journal-title":"IEEE Trans. Comput. Imag"},{"key":"35763_CR17","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ins.2020.04.035","volume":"529","author":"J Li","year":"2020","unstructured":"Li, J., Huo, H., Liu, K. & Li, C. Infrared and visible image fusion using dual discriminators generative adversarial networks with wasserstein distance. Inf. Sci. 529, 28\u201341 (2020).","journal-title":"Inf. Sci."},{"key":"35763_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z. et al. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 10012\u201310022 (2021).","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"35763_CR19","doi-asserted-by":"publisher","first-page":"5413","DOI":"10.1109\/TMM.2022.3192661","volume":"25","author":"W Tang","year":"2022","unstructured":"Tang, W., He, F. & Liu, Y. YDTR: Infrared and visible image fusion via Y-shape dynamic transformer. IEEE Transactions on Multimed. 25, 5413\u20135428 (2022).","journal-title":"IEEE Transactions on Multimed."},{"key":"35763_CR20","doi-asserted-by":"publisher","first-page":"12797","DOI":"10.1609\/aaai.v34i07.6975","volume":"34","author":"H Zhang","year":"2020","unstructured":"Zhang, H. et al. Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 12797\u201312804 (2020).","journal-title":"Proc. AAAI conference on artificial intelligence"},{"key":"35763_CR21","doi-asserted-by":"publisher","first-page":"2761","DOI":"10.1007\/s11263-021-01501-8","volume":"129","author":"H Zhang","year":"2021","unstructured":"Zhang, H. & Ma, J. SDNet: A versatile squeeze-and-decomposition network for real-time image fusion. Int. J. Comput. Vis. 129, 2761\u20132785 (2021).","journal-title":"Int. J. Comput. Vis."},{"key":"35763_CR22","first-page":"1","volume":"70","author":"J Ma","year":"2021","unstructured":"Ma, J. et al. STDFusionNet: An infrared and visible image fusion network based on salient target detection. IEEE Trans. Instrum. Meas 70, 1\u201313 (2021).","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"35763_CR23","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.inffus.2020.11.009","volume":"69","author":"Y Long","year":"2021","unstructured":"Long, Y. et al. RXDNFuse: A aggregated residual dense network for infrared and visible image fusion. Inf. Fusion 69, 128\u2013141 (2021).","journal-title":"Inf. Fusion"},{"key":"35763_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"35763_CR25","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 4700\u20134708 (2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"35763_CR26","doi-asserted-by":"publisher","first-page":"4070","DOI":"10.1109\/TIP.2021.3069339","volume":"30","author":"H Li","year":"2021","unstructured":"Li, H. et al. Different input resolutions and arbitrary output resolution: A meta learning-based deep framework for infrared and visible image fusion. IEEE Trans. Image Process. 30,\u00a04070\u20134083 (2021).","journal-title":"IEEE Trans. Image Process"},{"key":"35763_CR27","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","volume":"82","author":"L Tang","year":"2022","unstructured":"Tang, L., Yuan, J. & Ma, J. Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network. Inf. Fusion 82, 28\u201342 (2022).","journal-title":"Inf. Fusion"},{"key":"35763_CR28","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1109\/JAS.2022.106082","volume":"9","author":"L Tang","year":"2022","unstructured":"Tang, L. et al. SuperFusion: A versatile image registration and fusion network with semantic awareness. IEEE\/CAA J.\u00a0Autom. Sinica 9, 2121\u20132137 (2022).","journal-title":"IEEE\/CAA J.\u00a0Autom. Sinica"},{"key":"35763_CR29","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","volume":"83","author":"L Tang","year":"2022","unstructured":"Tang, L. et al. PIAFusion: A progressive infrared and visible image fusion network based on illumination aware. Inf. Fusion 83, 79\u201392 (2022).","journal-title":"Inf. Fusion"},{"key":"35763_CR30","volume-title":"PFCFuse: A Poolformer and CNN fusion network for Infrared-Visible Image Fusion","author":"X Hu","year":"2024","unstructured":"Hu, X., Liu, Y. & Yang, F. PFCFuse: A Poolformer and CNN fusion network for Infrared-Visible Image Fusion. IEEE Trans. Instrum. Meas 73, 1\u201314 (2024)"},{"key":"35763_CR31","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhou, N., Wan, W. & Huang, S. MACCNet: Multiscale Attention and Cross-Convolutional Network for Infrared and Visible Image Fusion. IEEE Sensors J. (2024).","DOI":"10.1109\/JSEN.2024.3385638"},{"key":"35763_CR32","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","volume":"48","author":"J Ma","year":"2019","unstructured":"Ma, J. et al. FusionGAN: A generative adversarial network for infrared and visible image fusion. Inf. fusion 48, 11\u201326 (2019).","journal-title":"Inf. fusion"},{"key":"35763_CR33","doi-asserted-by":"publisher","first-page":"4980","DOI":"10.1109\/TIP.2020.2977573","volume":"29","author":"J Ma","year":"2020","unstructured":"Ma, J. et al. DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans. Image Process 29, 4980\u20134995 (2020).","journal-title":"IEEE Trans. Image Process"},{"key":"35763_CR34","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1109\/TMM.2020.2997127","volume":"23","author":"J Li","year":"2020","unstructured":"Li, J. et al. AttentionFGAN: Infrared and visible image fusion using attention-based generative adversarial networks. IEEE Trans. Multimedia 23, 1383\u20131396 (2020).","journal-title":"IEEE Trans. Multimedia"},{"key":"35763_CR35","first-page":"1","volume":"70","author":"J Ma","year":"2020","unstructured":"Ma, J. et al. GANMcC: A generative adversarial network with multiclassification constraints for infrared and visible image fusion. IEEE Trans. Instrum. Meas 70, 1\u201314 (2020).","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"35763_CR36","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1109\/TMM.2021.3129609","volume":"25","author":"H Zhou","year":"2021","unstructured":"Zhou, H. et al. Semantic-supervised infrared and visible image fusion via a dual-discriminator generative adversarial network. IEEE Trans. Multimedia 25, 635\u2013648 (2021).","journal-title":"IEEE Trans. Multimedia"},{"key":"35763_CR37","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1109\/TCSVT.2022.3206807","volume":"33","author":"Y Gao","year":"2022","unstructured":"Gao, Y., Ma, S. & Liu, J. DCDR-GAN: A densely connected disentangled representation generative adversarial network for infrared and visible image fusion. IEEE Trans. Circuits Syst. Video Technol 33, 549\u2013561 (2022).","journal-title":"IEEE Trans. Circuits Syst. Video Technol"},{"key":"35763_CR38","doi-asserted-by":"crossref","unstructured":"Liu, J. et al. Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 5802\u20135811 (2022).","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"35763_CR39","first-page":"1","volume":"72","author":"H Yin","year":"2023","unstructured":"Yin, H., Xiao, J. & Chen, H. CSPA-GAN: A cross-scale pyramid attention GAN for infrared and visible image fusion. IEEE Trans. Instrum. Meas 72, 1\u201311 (2023).","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"35763_CR40","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/TCSVT.2024.3460172","volume":"35","author":"Z Wang","year":"2025","unstructured":"Wang, Z. et al. FreqGAN: Infrared and Visible Image Fusion via Unified Frequency Adversarial Learning. IEEE Trans. Circuits Syst. Video Technol 35, 728\u2013740 (2025).","journal-title":"IEEE Trans. Circuits Syst. Video Technol"},{"key":"35763_CR41","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","volume":"9","author":"J Ma","year":"2022","unstructured":"Ma, J. et al. SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer. IEEE\/CAA J.\u00a0Autom. Sinica 9, 1200\u20131217 (2022).","journal-title":"IEEE\/CAA J.\u00a0Autom. Sinica"},{"key":"35763_CR42","doi-asserted-by":"publisher","first-page":"3159","DOI":"10.1109\/TCSVT.2023.3234340","volume":"33","author":"W Tang","year":"2023","unstructured":"Tang, W. et al. DATFuse: Infrared and visible image fusion via dual attention transformer. IEEE Trans. Circuits Syst. Video Technol 33, 3159\u20133172 (2023).","journal-title":"IEEE Trans. Circuits Syst. Video Technol"},{"key":"35763_CR43","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/TCSVT.2023.3289170","volume":"34","author":"S Park","year":"2023","unstructured":"Park, S., Vien, A. G. & Lee, C. Cross-modal transformers for infrared and visible image fusion. IEEE Trans. Circuits Syst. Video Technol 34, 770\u2013785 (2023).","journal-title":"IEEE Trans. Circuits Syst. Video Technol"},{"key":"35763_CR44","doi-asserted-by":"crossref","unstructured":"Zhao, Z. et al. Cddfuse: Correlation-driven dual-branch feature decomposition for multi-modality image fusion. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 5906\u20135916 (2023).","DOI":"10.1109\/CVPR52729.2023.00572"},{"key":"35763_CR45","doi-asserted-by":"crossref","unstructured":"Yi, X. et al. Text-if: Leveraging semantic text guidance for degradation-aware and interactive image fusion. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 27026\u201327035 (2024).","DOI":"10.1109\/CVPR52733.2024.02552"},{"key":"35763_CR46","doi-asserted-by":"crossref","unstructured":"Liu, J. et al. PromptFusion: Harmonized semantic prompt learning for infrared and visible image fusion. IEEE\/CAA J.\u00a0Autom. Sinica\u00a0(2024).","DOI":"10.1109\/JAS.2024.124878"},{"key":"35763_CR47","doi-asserted-by":"crossref","unstructured":"Ying, Z. et al. A new low-light image enhancement algorithm using camera response model. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 3015\u20133022 (2017).","DOI":"10.1109\/ICCVW.2017.356"},{"key":"35763_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.107905","volume":"132","author":"Y Zhou","year":"2024","unstructured":"Zhou, Y. et al. ASFusion: Adaptive visual enhancement and structural patch decomposition for infrared and visible image fusion. Eng. Appl. Artif. Intell. 132, 107905 (2024).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"35763_CR49","doi-asserted-by":"crossref","unstructured":"Mao, X. et al. Least squares generative adversarial networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2794\u20132802 (2017).","DOI":"10.1109\/ICCV.2017.304"},{"key":"35763_CR50","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","volume":"44","author":"H Xu","year":"2020","unstructured":"Xu, H. et al. U2Fusion: A unified unsupervised image fusion network. IEEE Trans. Pattern Anal. Mach. Intell. 44, 502\u2013518 (2020).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"35763_CR51","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.dib.2017.09.038","volume":"15","author":"A Toet","year":"2017","unstructured":"Toet, A. The TNO multiband image data collection. Data Brief 15, 249\u2013251 (2017).","journal-title":"Data Brief"},{"key":"35763_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102352","volume":"108","author":"X Liu","year":"2024","unstructured":"Liu, X. et al. A semantic-driven coupled network for infrared and visible image fusion. Inf. Fusion\u00a0 108, 102352 (2024).","journal-title":"Inf. Fusion\u00a0"},{"key":"35763_CR53","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1109\/26.477498","volume":"43","author":"AM Eskicioglu","year":"2002","unstructured":"Eskicioglu, A. M. & Fisher, P. S. Image quality measures and their performance. IEEE Trans. Commun. 43, 2959\u20132965 (2002).","journal-title":"IEEE Trans. Commun"},{"key":"35763_CR54","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.optcom.2014.12.032","volume":"341","author":"G Cui","year":"2015","unstructured":"Cui, G. et al. Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition. Opt. Commun. 341, 199\u2013209 (2015).","journal-title":"Opt. Commun."},{"key":"35763_CR55","doi-asserted-by":"publisher","DOI":"10.1117\/1.2945910","volume":"2","author":"JW Roberts","year":"2008","unstructured":"Roberts, J. W., Van Aardt, J. A. & Ahmed, F. B. Assessment of image fusion procedures using entropy, image quality, and multispectral classification. J. Appl. Remote. Sens. 2, 023522 (2008).","journal-title":"J. Appl. Remote. Sens."},{"key":"35763_CR56","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1049\/el:20020212","volume":"38","author":"G Qu","year":"2002","unstructured":"Qu, G., Zhang, D. & Yan, P. Information measure for performance of image fusion. Electron. letters 38, 313\u2013315 (2002).","journal-title":"Electron. letters"},{"key":"35763_CR57","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1088\/0957-0233\/8\/4\/002","volume":"8","author":"Y-J Rao","year":"1997","unstructured":"Rao, Y.-J. In-fibre bragg grating sensors. Meas. science technology 8, 355 (1997).","journal-title":"Meas. science technology"},{"key":"35763_CR58","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.inffus.2011.08.002","volume":"14","author":"Y Han","year":"2013","unstructured":"Han, Y., Cai, Y., Cao, Y. & Xu, X. A new image fusion performance metric based on visual information fidelity. Inf. fusion 14, 127\u2013135 (2013).","journal-title":"Inf. fusion"},{"key":"35763_CR59","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 779\u2013788 (2016).","DOI":"10.1109\/CVPR.2016.91"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-35763-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-35763-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-35763-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T23:03:49Z","timestamp":1770764629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-35763-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,18]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["35763"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-35763-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,18]]},"assertion":[{"value":"18 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"5666"}}