{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T00:23:51Z","timestamp":1723335831856},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"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":["SIViP"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11760-024-03350-7","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T13:01:55Z","timestamp":1718974915000},"page":"6769-6782","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An effective reconstructed pyramid crosspoint fusion for multimodal\u00a0infrared\u00a0and\u00a0visible images"],"prefix":"10.1007","volume":"18","author":[{"given":"P.","family":"Murugeswari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N.","family":"Kopperundevi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Annalakshmi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. Scinthia","family":"Clarinda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"3350_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2022.107078","volume":"156","author":"B Wang","year":"2022","unstructured":"Wang, B., Zou, Y., Zhang, L., Li, Y., Chen, Q., Zuo, C.: Multimodal super-resolution reconstruction of infrared and visible images via deep learning. Optics Lasers Eng. 156, 107078 (2022)","journal-title":"Optics Lasers Eng."},{"key":"3350_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2022.108637","volume":"200","author":"FG Veshki","year":"2022","unstructured":"Veshki, F.G., Ouzir, N., Vorobyov, S.A., Ollila, E.: Multimodal Image Fusion via coupled feature learning. Signal Process. 200, 108637 (2022)","journal-title":"Signal Process."},{"key":"3350_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101828","volume":"98","author":"D Wang","year":"2023","unstructured":"Wang, D., Liu, J., Liu, R., Fan, X.: An interactively reinforced paradigm for joint infrared-visible image fusion and saliency object detection. Info. Fusion. 98, 101828 (2023)","journal-title":"Info. Fusion."},{"key":"3350_CR4","doi-asserted-by":"publisher","first-page":"914","DOI":"10.3390\/e25060914","volume":"25","author":"Y Jiang","year":"2023","unstructured":"Jiang, Y., Liu, Y., Zhan, W., Zhu, D.: Improved thermal infrared image super-resolution reconstruction method base on multimodal sensor fusion. Entropy 25, 914 (2023)","journal-title":"Entropy"},{"key":"3350_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3273451","author":"D Rao","year":"2024","unstructured":"Rao, D., Xu, T., Wu, X.-J.: TGFUSE: an infrared and visible image fusion approach based on transformer and generative adversarial network. IEEE Trans. Image Process. (2024). https:\/\/doi.org\/10.1109\/TIP.2023.3273451","journal-title":"IEEE Trans. Image Process."},{"key":"3350_CR6","volume":"29","author":"B Meher","year":"2022","unstructured":"Meher, B., Agrawal, S., Panda, R., Dora, L., Abraham, A.: Visible and infrared image fusion using an efficient adaptive transition region extraction technique. Eng. Sci. Technol. Int. J. 29, 101037 (2022)","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"3350_CR7","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s40747-022-00792-9","volume":"9","author":"P Guo","year":"2022","unstructured":"Guo, P., Xie, G., Li, R., Hu, H.: Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain. Comp. Intell. Syst. 9, 317\u2013328 (2022)","journal-title":"Comp. Intell. Syst."},{"key":"3350_CR8","unstructured":"Yuan, Y., Wu, J., Jing, Z., Leung, H., Pan, H.: Multimodal image fusion based on hybrid cnn-transformer and non-local cross-modal attention. arXiv preprint arXiv:2210.09847. (2022)"},{"key":"3350_CR9","first-page":"5503","volume":"70","author":"K Bhalla","year":"2022","unstructured":"Bhalla, K., Koundal, D., Bhatia, S., Khalid Imam Rahmani, M., Tahir, M.: Fusion of infrared and visible images using fuzzy based Siamese convolutional network. Comput. Mater. Contin. 70, 5503\u20135518 (2022)","journal-title":"Comput. Mater. Contin."},{"key":"3350_CR10","doi-asserted-by":"publisher","first-page":"660","DOI":"10.3390\/rs15030660","volume":"15","author":"J Wu","year":"2023","unstructured":"Wu, J., Shen, T., Wang, Q., Tao, Z., Zeng, K., Song, J.: Local adaptive illumination-driven input-level fusion for infrared and visible object detection. Remote Sens. 15, 660 (2023)","journal-title":"Remote Sens."},{"key":"3350_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3390\/s24010020","volume":"24","author":"S Lu","year":"2023","unstructured":"Lu, S., Ye, X., Rao, J., Li, F., Liu, S.: TDDFusion: a target-driven dual branch network for infrared and visible image fusion. Sensors. 24, 20 (2023)","journal-title":"Sensors."},{"key":"3350_CR12","doi-asserted-by":"publisher","first-page":"100327","DOI":"10.1016\/j.dajour.2023.100327","volume":"9","author":"S Kalamkar","year":"2023","unstructured":"Kalamkar, S.: Multimodal image fusion: a systematic review. Decis. Anal. J. 9, 100327 (2023)","journal-title":"Decis. Anal. J."},{"key":"3350_CR13","doi-asserted-by":"publisher","first-page":"718","DOI":"10.3390\/e25050718","volume":"25","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Zhou, X., Zhong, W.: Multi-modality image fusion and object detection based on semantic information. Entropy 25, 718 (2023)","journal-title":"Entropy"},{"key":"3350_CR14","doi-asserted-by":"publisher","first-page":"10891","DOI":"10.3390\/app131910891","volume":"13","author":"Y Luo","year":"2023","unstructured":"Luo, Y., Luo, Z.: Infrared and visible image fusion: Methods, datasets, applications, and prospects. Appl. Sci. 13, 10891 (2023)","journal-title":"Appl. Sci."},{"key":"3350_CR15","doi-asserted-by":"publisher","first-page":"3396","DOI":"10.3390\/app13063396","volume":"13","author":"Y Wu","year":"2023","unstructured":"Wu, Y., Liu, C.: A method of aerial multi-modal image registration for a low-visibility approach based on virtual reality fusion. Appl. Sci. 13, 3396 (2023)","journal-title":"Appl. Sci."},{"key":"3350_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108108","volume":"186","author":"L Ren","year":"2021","unstructured":"Ren, L., Pan, Z., Cao, J., Zhang, H., Wang, H.: Infrared and visible image fusion based on edge-preserving guided filter and infrared feature decomposition. Signal Process. 186, 108108 (2021)","journal-title":"Signal Process."},{"key":"3350_CR17","doi-asserted-by":"publisher","first-page":"3233","DOI":"10.3390\/rs14133233","volume":"14","author":"X Liu","year":"2022","unstructured":"Liu, X., Gao, H., Miao, Q., Xi, Y., Ai, Y., Gao, D.: MFST: Multi-modal feature self-adaptive transformer for infrared and visible image fusion. Remote Sens. 14, 3233 (2022)","journal-title":"Remote Sens."},{"key":"3350_CR18","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. Info. Fusion. 83\u201384, 79\u201392 (2022)","journal-title":"Info. Fusion."},{"key":"3350_CR19","doi-asserted-by":"publisher","first-page":"11040","DOI":"10.1109\/TPAMI.2023.3268209","volume":"45","author":"H Li","year":"2023","unstructured":"Li, H., Xu, T., Wu, X.-J., Lu, J., Kittler, J.: LRRNet: A novel representation learning guided fusion network for infrared and visible images. IEEE Trans. Pattern Anal. Mach. Intell. 45, 11040\u201311052 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3350_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2023.104032","volume":"137","author":"X Zhang","year":"2023","unstructured":"Zhang, X., Liu, G., Huang, L., Ren, Q., Bavirisetti, D.P.: IVOMFuse: an image fusion method based on infrared-to-visible object mapping. Digit. Signal Process. 137, 104032 (2023)","journal-title":"Digit. Signal Process."},{"key":"3350_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2022.168817","volume":"259","author":"X Zhou","year":"2022","unstructured":"Zhou, X., Liu, G., Zhang, X., Prasad, B.D., Gu, X., Li, Y.: Re2FAD: A differential image registration and robust image fusion method framework for power thermal anomaly detection. Optik 259, 168817 (2022)","journal-title":"Optik"},{"key":"3350_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/TG.2023.3263001","author":"X Gu","year":"2023","unstructured":"Gu, X., Liu, G., Zhang, X., Tang, L., Zhou, X., Qiu, W.: Infrared-visible synthetic data from game engine for image fusion improvement. IEEE Trans. Games (2023). https:\/\/doi.org\/10.1109\/TG.2023.3263001","journal-title":"IEEE Trans. Games"},{"key":"3350_CR23","doi-asserted-by":"publisher","first-page":"11923","DOI":"10.1109\/ACCESS.2023.3242050","volume":"11","author":"Z Li","year":"2023","unstructured":"Li, Z., Liu, H., Cheng, L., Jia, X.: Image denoising algorithm based on gradient domain guided filtering and NSST. IEEE Access. 11, 11923\u201311933 (2023)","journal-title":"IEEE Access."},{"key":"3350_CR24","doi-asserted-by":"crossref","unstructured":"Georgescu, M.-I., Ionescu, R.T., Miron, A.-I., Savencu, O., Ristea, N.-C., Verga, N., Khan, F.S.: Multimodal multi-head convolutional attention with various kernel sizes for medical image super-resolution. In 2023 IEEE\/CVF Winter Conference Appl. Computer Vision (WACV) (2023)","DOI":"10.1109\/WACV56688.2023.00223"},{"key":"3350_CR25","doi-asserted-by":"publisher","first-page":"106626","DOI":"10.1016\/j.compbiomed.2023.106626","volume":"154","author":"Q Xu","year":"2023","unstructured":"Xu, Q., Ma, Z., He, N., Duan, W.: DCSAU-net: a deeper and more compact split-attention U-Net for medical image segmentation. Comput. Biol. Med. 154, 106626 (2023)","journal-title":"Comput. Biol. Med."},{"key":"3350_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, C., Mendieta, M., Chen, C.: Poster: a pyramid cross-fusion transformer network for facial expression recognition. In 2023 IEEE\/CVF International Conference Comput. Vision Workshops (ICCVW) (2023)","DOI":"10.1109\/ICCVW60793.2023.00339"},{"key":"3350_CR27","doi-asserted-by":"publisher","first-page":"3412","DOI":"10.3390\/rs14143412","volume":"14","author":"Z Zuo","year":"2022","unstructured":"Zuo, Z., Tong, X., Wei, J., Su, S., Wu, P., Guo, R., Sun, B.: AFFPN: Attention fusion feature pyramid network for small infrared target detection. Remote Sens. 14, 3412 (2022)","journal-title":"Remote Sens."},{"key":"3350_CR28","doi-asserted-by":"publisher","first-page":"134826","DOI":"10.1109\/ACCESS.2021.3116304","volume":"9","author":"N Engel","year":"2021","unstructured":"Engel, N., Belagiannis, V., Dietmayer, K.: Point transformer. IEEE Access 9, 134826\u2013134840 (2021)","journal-title":"IEEE Access"},{"key":"3350_CR29","doi-asserted-by":"publisher","first-page":"122069","DOI":"10.1109\/ACCESS.2023.3328248","volume":"11","author":"MV Anaraki","year":"2023","unstructured":"Anaraki, M.V., Farzin, S.: Humboldt squid optimization algorithm (HSOA): a novel nature-inspired technique for solving optimization problems. IEEE Access 11, 122069\u2013122115 (2023)","journal-title":"IEEE Access"},{"key":"3350_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2022.104435","volume":"127","author":"H Tang","year":"2022","unstructured":"Tang, H., Liu, G., Tang, L., Bavirisetti, D.P., Wang, J.: MdedFusion: A multi-level detail enhancement decomposition method for infrared and visible image fusion. Infrared Phys. Technol. 127, 104435 (2022)","journal-title":"Infrared Phys. Technol."},{"key":"3350_CR31","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1049\/ipr2.12431","volume":"16","author":"H Wang","year":"2022","unstructured":"Wang, H., An, W., Li, L., Li, C., Zhou, D.: Infrared and visible image fusion based on multi-channel Convolutional Neural Network. IET Image Process. 16, 1575\u20131584 (2022)","journal-title":"IET Image Process."},{"key":"3350_CR32","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1016\/j.inffus.2022.10.034","volume":"91","author":"L Tang","year":"2023","unstructured":"Tang, L., Xiang, X., Zhang, H., Gong, M., Ma, J.: Divfusion: Darkness-free infrared and visible image fusion. Info. Fusion. 91, 477\u2013493 (2023)","journal-title":"Info. Fusion."},{"key":"3350_CR33","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Bai, H., Zhang, J., Zhang, Y., Xu, S., Lin, Z., Timofte, R., Van Gool, L.: CDDFuse: correlation-driven dual-branch feature decomposition for multi-modality image fusion. In 2023 IEEE\/CVF Conference Comput. Vision Pattern Recognition (CVPR) (2023)","DOI":"10.1109\/CVPR52729.2023.00572"},{"key":"3350_CR34","doi-asserted-by":"publisher","first-page":"126117","DOI":"10.1109\/ACCESS.2022.3226564","volume":"10","author":"S Park","year":"2022","unstructured":"Park, S., Lee, C.: Multiscale progressive fusion of infrared and visible images. IEEE Access. 10, 126117\u2013126132 (2022)","journal-title":"IEEE Access."},{"issue":"5","key":"3350_CR35","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1007\/s11263-023-01952-1","volume":"132","author":"J Liu","year":"2023","unstructured":"Liu, J., Lin, R., Wu, G., Liu, R., Luo, Z., Fan, X.: CoCoNet: coupled contrastive learning network with multi-level feature ensemble for multi-modality image fusion. Int. J. Comput. Vision 132(5), 1748\u20131775 (2023)","journal-title":"Int. J. Comput. Vision"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03350-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03350-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03350-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T10:22:09Z","timestamp":1723285329000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03350-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":35,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["3350"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03350-7","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2024,6,21]]},"assertion":[{"value":"15 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2024","order":4,"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 that they have no potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All applicable institutional and\/or national guidelines for the care and use of animals were followed.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"For this type of analysis formal consent is not needed.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}