{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T13:25:35Z","timestamp":1769261135077,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557639","type":"print"},{"value":"9789819557646","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5764-6_27","type":"book-chapter","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:07:54Z","timestamp":1769148474000},"page":"395-409","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GDTFusion: Gated Dual-Branch Attention Transformer Network for\u00a0Infrared and\u00a0Visible Image Fusion"],"prefix":"10.1007","author":[{"given":"Xuedong","family":"Guo","sequence":"first","affiliation":[]},{"given":"Maoyong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yingying","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Mingli","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Lianqing","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,24]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.neunet.2023.12.031","volume":"171","author":"D Peng","year":"2024","unstructured":"Peng, D., Zhou, W., Pan, J., Wang, D.: MSEDNet: multi-scale fusion and edge-supervised network for RGB-T salient object detection. Neural Netw. 171, 410\u2013422 (2024)","journal-title":"Neural Netw."},{"key":"27_CR2","doi-asserted-by":"publisher","first-page":"6348","DOI":"10.1109\/TMM.2023.3349072","volume":"26","author":"Y Lv","year":"2024","unstructured":"Lv, Y., Liu, Z., Li, G.: Context-aware interaction network for RGB-T semantic segmentation. IEEE Trans. Multimedia 26, 6348\u20136360 (2024)","journal-title":"IEEE Trans. Multimedia"},{"issue":"12","key":"27_CR3","doi-asserted-by":"publisher","first-page":"7631","DOI":"10.1109\/TSMC.2023.3298921","volume":"53","author":"W Zhou","year":"2023","unstructured":"Zhou, W., Gong, T., Lei, J., Yu, L.: DBCNet: dynamic bilateral cross-fusion network for RGB-T urban scene understanding in intelligent vehicles. IEEE Trans. Syst. Man Cybern. Syst. 53(12), 7631\u20137641 (2023)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"issue":"7","key":"27_CR4","doi-asserted-by":"publisher","first-page":"5519","DOI":"10.1109\/TCSVT.2024.3352573","volume":"34","author":"Y He","year":"2024","unstructured":"He, Y., Ma, Z., Wei, X., Gong, Y.: Knowledge synergy learning for multi-modal tracking. IEEE Trans. Circ. Syst. Video Technol. 34(7), 5519\u20135532 (2024)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"issue":"10","key":"27_CR5","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1002\/jemt.23514","volume":"83","author":"W Tang","year":"2020","unstructured":"Tang, W., Liu, Y., Cheng, J., Li, C., Peng, H., Chen, X.: A phase congruency-based green fluorescent protein and phase contrast image fusion method in nonsubsampled shearlet transform domain. Microsc. Res. Tech. 83(10), 1225\u20131234 (2020)","journal-title":"Microsc. Res. Tech."},{"issue":"1","key":"27_CR6","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3390\/e22010118","volume":"22","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Yang, X., Zhang, R., Albertini, M.K., Celik, T., Jeon, G.: Entropy-based image fusion with joint sparse representation and rolling guidance filter. Entropy 22(1), 118 (2020)","journal-title":"Entropy"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Mitianoudis, N., Stathaki, T.: Pixel-based and region-based image fusion schemes using ICA bases. Inf. Fus. 8(2), 131\u2013142 (2007). Special Issue on Image Fusion: Advances in the State of the Art","DOI":"10.1016\/j.inffus.2005.09.001"},{"issue":"3","key":"27_CR8","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1109\/TCSVT.2020.2998696","volume":"31","author":"R Nie","year":"2021","unstructured":"Nie, R., Cao, J., Zhou, D., Qian, W.: Multi-source information exchange encoding with PCNN for medical image fusion. IEEE Trans. Circ. Syst. Video Technol. 31(3), 986\u20131000 (2021)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"issue":"11","key":"27_CR9","doi-asserted-by":"publisher","first-page":"4357","DOI":"10.1109\/TCSVT.2020.3047935","volume":"31","author":"MJ Tan","year":"2021","unstructured":"Tan, M.J., Gao, S.B., Xu, W.Z., Han, S.C.: Visible-infrared image fusion based on early visual information processing mechanisms. IEEE Trans. Circ. Syst. Video Technol. 31(11), 4357\u20134369 (2021)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"issue":"5","key":"27_CR10","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2019","unstructured":"Li, H., Wu, X.J.: DenseFuse: a fusion approach to infrared and visible images. IEEE Trans. Image Process. 28(5), 2614\u20132623 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"27_CR11","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","volume":"83\u201384","author":"L Tang","year":"2022","unstructured":"Tang, L., Yuan, J., Zhang, H., Jiang, X., Ma, J.: PIAFusion: a progressive infrared and visible image fusion network based on illumination aware. Inf. Fus. 83\u201384, 79\u201392 (2022)","journal-title":"Inf. Fus."},{"issue":"7","key":"27_CR12","doi-asserted-by":"publisher","first-page":"3159","DOI":"10.1109\/TCSVT.2023.3234340","volume":"33","author":"W Tang","year":"2023","unstructured":"Tang, W., He, F., Liu, Y., Duan, Y., Si, T.: DATFuse: infrared and visible image fusion via dual attention transformer. IEEE Trans. Circ. Syst. Video Technol. 33(7), 3159\u20133172 (2023)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"27_CR13","first-page":"1","volume":"70","author":"J Ma","year":"2021","unstructured":"Ma, J., Zhang, H., Shao, Z., Liang, P., Xu, H.: GANMcC: a generative adversarial network with multiclassification constraints for infrared and visible image fusion. IEEE Trans. Instrum. Meas. 70, 1\u201314 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"27_CR14","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.inffus.2021.06.002","volume":"76","author":"F Zhao","year":"2021","unstructured":"Zhao, F., Zhao, W., Yao, L., Liu, Y.: Self-supervised feature adaption for infrared and visible image fusion. Inf. Fus. 76, 189\u2013203 (2021)","journal-title":"Inf. Fus."},{"key":"27_CR15","doi-asserted-by":"publisher","first-page":"110774","DOI":"10.1016\/j.patcog.2024.110774","volume":"156","author":"M Xing","year":"2024","unstructured":"Xing, M., Liu, G., Tang, H., Qian, Y., Zhang, J.: CFNet: an infrared and visible image compression fusion network. Pattern Recogn. 156, 110774 (2024)","journal-title":"Pattern Recogn."},{"key":"27_CR16","doi-asserted-by":"publisher","first-page":"109295","DOI":"10.1016\/j.patcog.2022.109295","volume":"137","author":"W Tang","year":"2023","unstructured":"Tang, W., He, F., Liu, Y.: TCCFusion: an infrared and visible image fusion method based on transformer and cross correlation. Pattern Recogn. 137, 109295 (2023)","journal-title":"Pattern Recogn."},{"issue":"5","key":"27_CR17","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1109\/TCSVT.2019.2901809","volume":"30","author":"S Liu","year":"2020","unstructured":"Liu, S., Chen, J., Rahardja, S.: A new multi-focus image fusion algorithm and its efficient implementation. IEEE Trans. Circ. Syst. Video Technol. 30(5), 1374\u20131384 (2020)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"27_CR18","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1016\/j.ins.2017.09.010","volume":"432","author":"Z Zhu","year":"2018","unstructured":"Zhu, Z., Yin, H., Chai, Y., Li, Y., Qi, G.: A novel multi-modality image fusion method based on image decomposition and sparse representation. Inf. Sci. 432, 516\u2013529 (2018)","journal-title":"Inf. Sci."},{"issue":"2","key":"27_CR19","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s11704-014-2328-2","volume":"8","author":"Y Lu","year":"2014","unstructured":"Lu, Y., Wang, F., Luo, X., Liu, F.: Novel infrared and visible image fusion method based on independent component analysis. Front. Comp. Sci. 8(2), 243\u2013254 (2014). https:\/\/doi.org\/10.1007\/s11704-014-2328-2","journal-title":"Front. Comp. Sci."},{"key":"27_CR20","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.inffus.2014.09.004","volume":"24","author":"Y Liu","year":"2015","unstructured":"Liu, Y., Liu, S., Wang, Z.: A general framework for image fusion based on multi-scale transform and sparse representation. Inf. Fus. 24, 147\u2013164 (2015)","journal-title":"Inf. Fus."},{"issue":"8","key":"27_CR21","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.1109\/TCYB.2014.2355140","volume":"45","author":"X Qin","year":"2015","unstructured":"Qin, X., Shen, J., Mao, X., Li, X., Jia, Y.: Robust match fusion using optimization. IEEE Trans. Cybern. 45(8), 1549\u20131560 (2015)","journal-title":"IEEE Trans. Cybern."},{"key":"27_CR22","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., Li, X., Luo, L., Mei, X., Ma, J.: Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Inf. Sci. 508, 64\u201378 (2020)","journal-title":"Inf. Sci."},{"key":"27_CR23","doi-asserted-by":"publisher","first-page":"4733","DOI":"10.1109\/TIP.2020.2975984","volume":"29","author":"H Li","year":"2020","unstructured":"Li, H., Wu, X.J., Kittler, J.: MDLatLRR: a novel decomposition method for infrared and visible image fusion. IEEE Trans. Image Process. 29, 4733\u20134746 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"27_CR24","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.inffus.2015.11.003","volume":"30","author":"Z Zhou","year":"2016","unstructured":"Zhou, Z., Wang, B., Li, S., Dong, M.: Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters. Inf. Fus. 30, 15\u201326 (2016)","journal-title":"Inf. Fus."},{"key":"27_CR25","first-page":"824","volume":"7","author":"H Xu","year":"2021","unstructured":"Xu, H., Zhang, H., Ma, J.: Classification saliency-based rule for visible and infrared image fusion. IEEE Trans. Comput. Imag. 7, 824\u2013836 (2021)","journal-title":"IEEE Trans. Comput. Imag."},{"issue":"6","key":"27_CR26","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.1109\/TCSVT.2021.3109895","volume":"32","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Wang, J., Wu, Y., Xu, J., Zhang, X.: UNFusion: a unified multi-scale densely connected network for infrared and visible image fusion. IEEE Trans. Circ. Syst. Video Technol. 32(6), 3360\u20133374 (2022)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"issue":"7","key":"27_CR27","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","volume":"9","author":"J Ma","year":"2022","unstructured":"Ma, J., Tang, L., Fan, F., Huang, J., Mei, X., Ma, Y.: SwinFusion: cross-domain long-range learning for general image fusion via Swin transformer. IEEE\/CAA J. Autom. Sinica 9(7), 1200\u20131217 (2022)","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"key":"27_CR28","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., Yu, W., Liang, P., Li, C., Jiang, J.: FusionGan: a generative adversarial network for infrared and visible image fusion. Inf. Fus. 48, 11\u201326 (2019)","journal-title":"Inf. Fus."},{"key":"27_CR29","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. Fus. 73, 72\u201386 (2021)","journal-title":"Inf. Fus."},{"key":"27_CR30","doi-asserted-by":"publisher","first-page":"5413","DOI":"10.1109\/TMM.2022.3192661","volume":"25","author":"W Tang","year":"2023","unstructured":"Tang, W., He, F., Liu, Y.: YDTR: infrared and visible image fusion via Y-shape dynamic transformer. IEEE Trans. Multimedia 25, 5413\u20135428 (2023)","journal-title":"IEEE Trans. Multimedia"},{"issue":"2","key":"27_CR31","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1109\/TCSVT.2022.3206807","volume":"33","author":"Y Gao","year":"2023","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. Circ. Syst. Video Technol. 33(2), 549\u2013561 (2023)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"27_CR32","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: Simple and efficient design for semantic segmentation with transformers. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 12077\u201312090. Curran Associates, Inc. (2021)"},{"key":"27_CR33","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Multi-interactive feature learning and a full-time multi-modality benchmark for image fusion and segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8115\u20138124 (2023)","DOI":"10.1109\/ICCV51070.2023.00745"},{"key":"27_CR34","doi-asserted-by":"crossref","unstructured":"Ha, Q., Watanabe, K., Karasawa, T., Ushiku, Y., Harada, T.: MFNet: towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5108\u20135115 (2017)","DOI":"10.1109\/IROS.2017.8206396"},{"key":"27_CR35","doi-asserted-by":"publisher","first-page":"128116","DOI":"10.1016\/j.neucom.2024.128116","volume":"600","author":"G Yang","year":"2024","unstructured":"Yang, G., Li, J., Lei, H., Gao, X.: A multi-scale information integration framework for infrared and visible image fusion. Neurocomputing 600, 128116 (2024)","journal-title":"Neurocomputing"},{"key":"27_CR36","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. Fus. 82, 28\u201342 (2022)","journal-title":"Inf. Fus."},{"key":"27_CR37","doi-asserted-by":"crossref","unstructured":"Yang, C., Luo, X., Zhang, Z., Chen, Z., Jun, W.X.: KDFuse: a high-level vision task-driven infrared and visible image fusion method based on cross-domain knowledge distillation. Inf. Fus. 118, 102944 (2025)","DOI":"10.1016\/j.inffus.2025.102944"},{"key":"27_CR38","doi-asserted-by":"crossref","unstructured":"Lei, J., et al.: MLFuse: multi-scenario feature joint learning for multi-modality image fusion. IEEE Trans. Multimedia, 1\u201316 (2025)","DOI":"10.1109\/TMM.2025.3535355"},{"key":"27_CR39","doi-asserted-by":"publisher","first-page":"108929","DOI":"10.1016\/j.patcog.2022.108929","volume":"132","author":"M Xu","year":"2022","unstructured":"Xu, M., Tang, L., Zhang, H., Ma, J.: Infrared and visible image fusion via parallel scene and texture learning. Pattern Recogn. 132, 108929 (2022)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5764-6_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:07:58Z","timestamp":1769148478000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5764-6_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557639","9789819557646"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5764-6_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"24 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}