{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T09:04:23Z","timestamp":1770541463832,"version":"3.49.0"},"reference-count":77,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GuangDong Basic and Applied Basic Research Foundation","award":["2024A1515010036"],"award-info":[{"award-number":["2024A1515010036"]}]},{"name":"GuangDong Basic and Applied Basic Research Foundation","award":["32102078"],"award-info":[{"award-number":["32102078"]}]},{"name":"GuangDong Basic and Applied Basic Research Foundation","award":["2023A04J0351"],"award-info":[{"award-number":["2023A04J0351"]}]},{"name":"National Natural Science Foundation of China","award":["2024A1515010036"],"award-info":[{"award-number":["2024A1515010036"]}]},{"name":"National Natural Science Foundation of China","award":["32102078"],"award-info":[{"award-number":["32102078"]}]},{"name":"National Natural Science Foundation of China","award":["2023A04J0351"],"award-info":[{"award-number":["2023A04J0351"]}]},{"name":"Guangzhou Municipal Science and Technology","award":["2024A1515010036"],"award-info":[{"award-number":["2024A1515010036"]}]},{"name":"Guangzhou Municipal Science and Technology","award":["32102078"],"award-info":[{"award-number":["32102078"]}]},{"name":"Guangzhou Municipal Science and Technology","award":["2023A04J0351"],"award-info":[{"award-number":["2023A04J0351"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Infrared and visible image fusion integrates complementary information from different modalities into a single image, providing sufficient imaging information for scene interpretation and downstream target recognition tasks. However, existing fusion methods often focus only on highlighting salient targets or preserving scene details, failing to effectively combine entire features from different modalities during the fusion process, resulting in underutilized features and poor overall fusion effects. To address these challenges, a global and local four-branch feature extraction image fusion network (GLFuse) is proposed. On one hand, the Super Token Transformer (STT) block, which is capable of rapidly sampling and predicting super tokens, is utilized to capture global features in the scene. On the other hand, a Detail Extraction Block (DEB) is developed to extract local features in the scene. Additionally, two feature fusion modules, namely the Attention-based Feature Selection Fusion Module (ASFM) and the Dual Attention Fusion Module (DAFM), are designed to facilitate selective fusion of features from different modalities. Of more importance, the various perceptual information of feature maps learned from different modality images at the different layers of a network is investigated to design a perceptual loss function to better restore scene detail information and highlight salient targets by treating the perceptual information separately. Extensive experiments confirm that GLFuse exhibits excellent performance in both subjective and objective evaluations. It deserves note that GLFuse effectively improves downstream target detection performance on a unified benchmark.<\/jats:p>","DOI":"10.3390\/rs16173246","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T07:59:40Z","timestamp":1725263980000},"page":"3246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["GLFuse: A Global and Local Four-Branch Feature Extraction Network for Infrared and Visible Image Fusion"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3360-1756","authenticated-orcid":false,"given":"Genping","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Zhuyong","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Silu","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Zhuowei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Heng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Automation, Guangdong University of Technology, Guangzhou 510006, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/TMM.2021.3057493","article-title":"Multi-focus image fusion based on multi-scale gradients and image matting","volume":"24","author":"Chen","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s13634-024-01139-x","article-title":"Deep video-based person re-identification (Deep Vid-ReID): Comprehensive survey","volume":"1","author":"Saad","year":"2024","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s13634-023-01002-5","article-title":"Decision-level fusion detection method of visible and infrared images under low light conditions","volume":"1","author":"Hu","year":"2023","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., and Jagersand, M. (2019, January 15\u201320). Basnet: Boundary-Aware Salient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00766"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1007\/s10489-020-01882-2","article-title":"TIRNet: Object detection in thermal infrared images for autonomous driving","volume":"51","author":"Dai","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ha, Q., Watanabe, K., Karasawa, T., Ushiku, Y., and Harada, T. (2017, January 24\u201328). MFNet: Towards Real-Time Semantic Segmentation for Autonomous Vehicles with Multi-Spectral Scenes. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206396"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","article-title":"Infrared and visible image fusion methods and applications: A survey","volume":"45","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.neucom.2017.01.006","article-title":"Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion","volume":"235","author":"Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.inffus.2010.03.002","article-title":"Performance comparison of different multi-resolution transforms for image fusion","volume":"12","author":"Li","year":"2011","journal-title":"Inf. Fusion"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1016\/j.patcog.2004.03.010","article-title":"A wavelet-based image fusion tutorial","volume":"37","author":"Pajares","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.infrared.2014.09.019","article-title":"Fusion method for infrared and visible images by using non-negative sparse representation","volume":"67","author":"Wang","year":"2014","journal-title":"Infrared Phys. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.inffus.2017.05.006","article-title":"Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review","volume":"40","author":"Zhang","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/LSP.2016.2618776","article-title":"Image fusion with convolutional sparse representation","volume":"23","author":"Liu","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_14","unstructured":"Lewis, J.J., O\u2019callaghan, R.J., Nikolov, S.G., Bull, D.R., and Canagarajah, C.N. (July, January 28). Region-Based Image Fusion Using Complex Wavelets. Proceedings of the 7th International Conference on Information Fusion, Stockholm, Sweden."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.inffus.2018.07.010","article-title":"A survey on region based image fusion methods","volume":"48","author":"Meher","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","article-title":"DenseFuse: A fusion approach to infrared and visible images","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","article-title":"U2Fusion: A unified unsupervised image fusion network","volume":"44","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Xu, S., Zhang, C., Liu, J., Li, P., and Zhang, J. (2020). DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion. arXiv.","DOI":"10.24963\/ijcai.2020\/135"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s11263-021-01501-8","article-title":"SDNet: A versatile squeeze-and-decomposition network for real-time image fusion","volume":"129","author":"Zhang","year":"2021","journal-title":"Int. J. Comput. Vision."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021, January 11\u201317). Swinir: Image Restoration Using Swin Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_21","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rao, D., Xu, T., and Wu, X.J. (2023). Tgfuse: An infrared and visible image fusion approach based on transformer and generative adversarial network. arXiv.","DOI":"10.1109\/TIP.2023.3273451"},{"key":"ref_24","unstructured":"Fu, Y., Xu, T.Y., Wu, X.J., Fu, Y., Xu, T., Wu, X., and Kittler, J. (2021). Ppt Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Qu, L., Liu, S., Wang, M., and Song, Z. (2021). Transmef: A Transformer-Based Multi-Exposure Image Fusion Framework Using Self-Supervised Multi-Task Learning. arXiv.","DOI":"10.2139\/ssrn.4130858"},{"key":"ref_26","first-page":"5012314","article-title":"CGTF: Convolution-guided transformer for infrared and visible image fusion","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, H., and Nie, R. (2021, January 24\u201326). DNDT: Infrared and Visible Image Fusion via Densenet and Dual-Transformer. Proceedings of the 2021 International Conference on Information Technology and Biomedical Engineering (ICITBE), Nanchang, China.","DOI":"10.1109\/ICITBE54178.2021.00025"},{"key":"ref_28","unstructured":"Huang, J., Li, X., Tan, T., Li, X., and Ye, T. (2024). MMA-UNet: A Multi-Modal Asymmetric UNet Architecture for Infrared and Visible Image Fusion. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Feng, S., Wu, C., Lin, C., and Huang, M. (2023). RADFNet: An infrared and visible image fusion framework based on distributed network. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.1056711"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, J., Yafei, Z., and Fan, L. (2023). Infrared and visible image fusion with edge detail implantation. Front. Phys., 11.","DOI":"10.3389\/fphy.2023.1180100"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","article-title":"FusionGAN: A generative adversarial network for infrared and visible image fusion","volume":"48","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1109\/TIP.2020.2977573","article-title":"DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion","volume":"29","author":"Ma","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/TMM.2020.2997127","article-title":"AttentionFGAN: Infrared and visible image fusion using attention-based generative adversarial networks","volume":"23","author":"Li","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1109\/TIM.2020.3005230","article-title":"NestFuse: An infrared and visible image fusion architecture based on nest connection and spatial\/channel attention models","volume":"69","author":"Li","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","article-title":"IFCNN: A general image fusion framework based on convolutional neural network","volume":"54","author":"Zhang","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_36","first-page":"12797","article-title":"Rethinking the Image Fusion: A Fast Unified Image Fusion Network Based on Proportional Maintenance of Gradient and Intensity","volume":"34","author":"Zhang","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","article-title":"Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network","volume":"82","author":"Tang","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_38","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.Y., Parmar, N., Shlens, J., Abbeel, P., and Vaswani, A. (2021). Bottleneck Transformers for Visual Recognition. arXiv.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, M., Peng, H., Fu, J., and Ling, H. (2021, January 10\u201317). Autoformer: Searching Transformers for Visual Recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01205"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-End Object Detection with Transformers. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_42","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable Detr: Deformable Transformers for End-to-End Object Detection. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Meinhardt, T., Kirillov, A., Leal-Taixe, L., and Feichtenhofer, C. (2021). Trackformer: Multi-object Tracking with Transformers. arXiv.","DOI":"10.1109\/CVPR52688.2022.00864"},{"key":"ref_44","first-page":"16743","article-title":"Swintrack: A simple and strong baseline for transformer tracking","volume":"35","author":"Lin","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_45","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv."},{"key":"ref_46","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, H., and Hu, Q. (2021). Transfuse: Fusing Transformers and Cnns for Medical Image Segmentation. Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021, Proceeding of the 24th International Conference, Strasbourg, France, 27 September\u20131 October 2021, Springer. Part I 24.","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.neucom.2023.01.033","article-title":"THFuse: An infrared and visible image fusion network using transformer and hybrid feature extractor","volume":"527","author":"Chen","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104405","DOI":"10.1016\/j.infrared.2022.104405","article-title":"TCPMFNet: An infrared and visible image fusion network with composite auto encoder and transformer\u2013convolutional parallel mixed fusion strategy","volume":"127","author":"Yi","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"109295","DOI":"10.1016\/j.patcog.2022.109295","article-title":"TCCFusion: An infrared and visible image fusion method based on transformer and cross correlation","volume":"137","author":"Tang","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_51","unstructured":"Huang, H., Zhou, X., Cao, J., Huang, H., Zhou, X., Cao, J., He, R., and Tan, T. (2022). Vision Transformer with Super Token Sampling. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"128116","DOI":"10.1016\/j.neucom.2024.128116","article-title":"A multi-scale information integration framework for infrared and visible image fusion","volume":"600","author":"Yang","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_53","first-page":"1","article-title":"MAFusion: Multiscale attention network for infrared and visible image fusion","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"102147","DOI":"10.1016\/j.inffus.2023.102147","article-title":"CrossFuse: A novel cross attention mechanism based infrared and visible image fusion approach","volume":"103","author":"Li","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016). Perceptual Losses for Real-Time Style Transfer and Super-Resolution. Computer Vision\u2013ECCV 2016, Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer. Part II.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_57","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"104242","DOI":"10.1016\/j.infrared.2022.104242","article-title":"Multi-scale unsupervised network for infrared and visible image fusion based on joint attention mechanism","volume":"125","author":"Xu","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_59","first-page":"12484","article-title":"Fusiondn: A Unified Densely Connected Network for Image Fusion","volume":"34","author":"Xu","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","article-title":"PIAFusion: A progressive infrared and visible image fusion network based on illumination aware","volume":"83","author":"Tang","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_61","unstructured":"Toet, A. (2014). TNO Image Fusion Dataset. Figshare."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.inffus.2021.02.023","article-title":"RFN-Nest: An end-to-end residual fusion network for infrared and visible images","volume":"73","author":"Li","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"104383","DOI":"10.1016\/j.infrared.2022.104383","article-title":"FLFuse-Net: A fast and lightweight infrared and visible image fusion network via feature flow and edge compensation for salient information","volume":"127","author":"Xue","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3159","DOI":"10.1109\/TCSVT.2023.3234340","article-title":"DATFuse: Infrared and visible image fusion via dual attention transformer","volume":"33","author":"Tang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhao, W., Xie, S., Zhao, F., He, Y., and Lu, H. (2023, January 17\u201324). Metafusion: Infrared and Visible Image Fusion via Meta-Feature Embedding from Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01341"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1007\/s11263-023-01952-1","article-title":"Coconet: Coupled contrastive learning network with multi-level feature ensemble for multi-modality image fusion","volume":"132","author":"Liu","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Bai, H., Zhu, Y., Zhang, J., Xu, S., Zhang, Y., Zhang, K., Meng, D., Timofte, R., and Van Gool, L. (2023, January 1\u20136). DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00742"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Liu, J., Fan, X., Huang, Z., Wu, G., Liu, R., Zhong, W., and Luo, Z. (2022, January 18\u201324). Target-Aware Dual Adversarial Learning and a Multi-Scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"023522","DOI":"10.1117\/1.2945910","article-title":"Assessment of image fusion procedures using entropy, image quality, and multispectral classification","volume":"2","author":"Roberts","year":"2008","journal-title":"J. Appl. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1109\/26.477498","article-title":"Image quality measures and their performance","volume":"43","author":"Eskicioglu","year":"1995","journal-title":"IEEE Trans. Commun."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1049\/el:20020212","article-title":"Information measure for performance of image fusion","volume":"38","author":"Qu","year":"2002","journal-title":"Electron. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1088\/0957-0233\/8\/4\/002","article-title":"In-fibre Bragg grating sensors","volume":"8","author":"Rao","year":"1997","journal-title":"Meas. Sci. Technol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.inffus.2011.08.002","article-title":"A new image fusion performance metric based on visual information fidelity","volume":"14","author":"Han","year":"2013","journal-title":"Inf. Fusion"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1016\/j.aeue.2015.09.004","article-title":"A new image quality metric for image fusion: The sum of the correlations of differences","volume":"69","author":"Aslantas","year":"2015","journal-title":"Aeu-Int. J. Electron. Commun."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.aqpro.2015.02.019","article-title":"A review of quality metrics for fused image","volume":"4","author":"Jagalingam","year":"2015","journal-title":"Aquat. Procedia"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1049\/el:20000267","article-title":"Objective image fusion performance measure","volume":"36","author":"Xydeas","year":"2000","journal-title":"Electron. Lett."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3246\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:46:53Z","timestamp":1760111213000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,1]]},"references-count":77,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173246"],"URL":"https:\/\/doi.org\/10.3390\/rs16173246","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,1]]}}}