{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T02:01:38Z","timestamp":1778810498493,"version":"3.51.4"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21JR7RA300"],"award-info":[{"award-number":["21JR7RA300"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open project of Gansu Provincial Research Center for Conservation of Dunhuang Cultural Heritage","award":["GDW2021YB15"],"award-info":[{"award-number":["GDW2021YB15"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["NO.61966022"],"award-info":[{"award-number":["NO.61966022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00138-022-01326-6","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T02:02:26Z","timestamp":1660096946000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-focus image fusion based on unsupervised learning"],"prefix":"10.1007","volume":"33","author":[{"given":"Kaijun","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3892-6939","authenticated-orcid":false,"given":"Yuan","family":"Mei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"issue":"02","key":"1326_CR1","first-page":"321","volume":"44","author":"Zhang Lixia","year":"2022","unstructured":"Lixia, Zhang, Guangping, Zeng, Zhaocheng, Xuan: Research Review of Multi-source Image Fusion Methods. Comput. Eng. Sci. 44(02), 321\u2013334 (2022)","journal-title":"Comput. Eng. Sci."},{"key":"1326_CR2","unstructured":"Shuaiqi, Liu, Jie, Wang, Yanling, An., Li Ziqi, Hu., Shaohai, Wang Wenfeng: Nonsubsampled Shearlet Domain Multifocus Image Fusion Based on CNN[J]. Journal of Zhengzhou University (Engineering Edition) 40(04), 36\u201341 (2019)"},{"key":"1326_CR3","unstructured":"Gang, Chen.: Research on Multi-Focus Image Fusion Algorithm[D]. China University of Mining and Technology, (2018)"},{"issue":"04","key":"1326_CR4","first-page":"965","volume":"43","author":"Nie Xixi","year":"2021","unstructured":"Xixi, Nie, Bin, Xiao, Xiuli, Bi., Weisheng, Li.: Multi-focus image fusion algorithm based on superpixel convolutional neural network. Electr. Inform. 43(04), 965\u2013973 (2021)","journal-title":"Electr. Inform."},{"issue":"01","key":"1326_CR5","first-page":"160","volume":"28","author":"Gu Jiang Feng","year":"2017","unstructured":"Jiang Feng, Gu., Qing, Hao Huizhen, Na, Li., Yanwen, Guo, Daozhi, Chen: Overview of content-based image segmentation methods. Softw. J. 28(01), 160\u2013183 (2017)","journal-title":"Softw. J."},{"issue":"2","key":"1326_CR6","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.inffus.2010.03.002","volume":"12","author":"S Li","year":"2011","unstructured":"Li, S., Yang, B., Hu, J.: Performance comparison of different multi-resolution transforms for image fusion. Inform. Fus. 12(2), 74\u201384 (2011)","journal-title":"Inform. Fus."},{"key":"1326_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.inffus.2021.04.005","volume":"75","author":"Y Mo","year":"2021","unstructured":"Mo, Y., Kang, X., Duan, P., et al.: Attribute filter based infrared and visible image fusion. Inform. Fus. 75, 41\u201354 (2021)","journal-title":"Inform. Fus."},{"issue":"6","key":"1326_CR8","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1007\/s11760-012-0361-x","volume":"7","author":"BK Shreyamsha Kumar","year":"2013","unstructured":"Shreyamsha Kumar, B.K.: Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform. Signal, Image and Video Process. 7(6), 1125\u20131143 (2013)","journal-title":"Signal, Image and Video Process."},{"key":"1326_CR9","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.inffus.2017.05.006","volume":"40","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Liu, Y., Blum, R.S., et al.: Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: a review. ProcessingInform. Fus. 40, 57\u201375 (2018)","journal-title":"ProcessingInform. Fus."},{"issue":"10","key":"1326_CR10","doi-asserted-by":"publisher","first-page":"12405","DOI":"10.1007\/s11042-017-4895-3","volume":"77","author":"N Paramanandham","year":"2018","unstructured":"Paramanandham, N., Rajendiran, K.: Multi sensor image fusion for surveillance applications using hybrid image fusion algorithm. Multimed. Tools Appl. 77(10), 12405\u201312436 (2018)","journal-title":"Multimed. Tools Appl."},{"key":"1326_CR11","doi-asserted-by":"crossref","unstructured":"Yang, L., Guo, B., Ni, W.: Multifocus image fusion algorithm based on contourlet decomposition and region statistics[C]\/\/Fourth international conference on image and graphics (ICIG 2007). IEEE, (2007): 707-712","DOI":"10.1109\/ICIG.2007.135"},{"key":"1326_CR12","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., Liu, Y., Sun, P., et al.: IFCNN: a general image fusion framework based on convolutional neural network. Inform. Fus. 54, 99\u2013118 (2020)","journal-title":"Inform. Fus."},{"key":"1326_CR13","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., Xu, H., Tian, X., et al.: Image fusion meets deep learning: a survey and perspective. Inform. Fus. 76, 323\u2013336 (2021)","journal-title":"Inform. Fus."},{"key":"1326_CR14","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.inffus.2016.12.001","volume":"36","author":"Y Liu","year":"2017","unstructured":"Liu, Y., Chen, X., Peng, H., et al.: Multi-focus image fusion with a deep convolutional neural network. Inform. Fus. 36, 191\u2013207 (2017)","journal-title":"Inform. Fus."},{"issue":"03","key":"1326_CR15","doi-asserted-by":"publisher","first-page":"500","DOI":"10.5768\/JAO202041.0302003","volume":"41","author":"Chen Qingjiang","year":"2020","unstructured":"Qingjiang, Chen, Zebai, Wang, Yuzhou, Chai: Improved VGG network multi-focus image fusion method. Appl. Opt. 41(03), 500\u2013507 (2020)","journal-title":"Appl. Opt."},{"issue":"07","key":"1326_CR16","doi-asserted-by":"crossref","first-page":"246","DOI":"10.3788\/LOP55.071015","volume":"55","author":"Chen Qingjiang","year":"2018","unstructured":"Qingjiang, Chen, Yi, Li., Yuzhou, Chai: A multifocus image fusion algorithm based on deep learning. Prog. Laser Optoelectron. 55(07), 246\u2013254 (2018)","journal-title":"Prog. Laser Optoelectron."},{"key":"1326_CR17","doi-asserted-by":"crossref","unstructured":"Ram Prabhakar, K., Sai Srikar, V., Venkatesh Babu R.: Deepfuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs[C]\/\/Proceedings of the IEEE international conference on computer vision. (2017): 4714-4722","DOI":"10.1109\/ICCV.2017.505"},{"issue":"5","key":"1326_CR18","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 Proc. 28(5), 2614\u20132623 (2018)","journal-title":"IEEE Trans. Image Proc."},{"issue":"11","key":"1326_CR19","doi-asserted-by":"publisher","first-page":"5793","DOI":"10.1007\/s00521-020-05358-9","volume":"33","author":"B Ma","year":"2021","unstructured":"Ma, B., Zhu, Y., Yin, X., et al.: Sesf-fuse: an unsupervised deep model for multi-focus image fusion. Neural Comput. Appl. 33(11), 5793\u20135804 (2021)","journal-title":"Neural Comput. Appl."},{"key":"1326_CR20","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.inffus.2020.08.022","volume":"66","author":"H Zhang","year":"2021","unstructured":"Zhang, H., Le, Z., Shao, Z., et al.: MFF-GAN: an unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion. Inform. Fus. 66, 40\u201353 (2021)","journal-title":"Inform. Fus."},{"key":"1326_CR21","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1109\/TCI.2021.3063872","volume":"7","author":"J Ma","year":"2021","unstructured":"Ma, J., Le, Z., Tian, X., et al.: SMFuse: Multi-focus image fusion via self-supervised mask-optimization. IEEE Trans. Comput. Imaging 7, 309\u2013320 (2021)","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"1","key":"1326_CR22","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","volume":"44","author":"H Xu","year":"2020","unstructured":"Xu, H., Ma, J., Jiang, J., et al.: U2Fusion: a unified unsupervised image fusion network. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 502\u2013518 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1326_CR23","doi-asserted-by":"crossref","unstructured":"Xu, H., Ma, J., Le, Z., et al.: Fusiondn: A unified densely connected network for image fusion[C]. In: Proceedings of the AAAI Conference on Artificial Intelligence. (2020) , 34(07): 12484-12491","DOI":"10.1609\/aaai.v34i07.6936"},{"key":"1326_CR24","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.inffus.2014.10.004","volume":"25","author":"M Nejati","year":"2015","unstructured":"Nejati, M., Samavi, S., Shirani, S.: Multi-focus image fusion using dictionary-based sparse representation. Inform. Fus. 25, 72\u201384 (2015)","journal-title":"Inform. Fus."},{"issue":"1","key":"1326_CR25","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2016","unstructured":"Zhao, H., Gallo, O., Frosio, I., et al.: Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3(1), 47\u201357 (2016)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"1326_CR26","unstructured":"Wang, Z., Simoncelli, E. P., Bovik, A. C.: Multiscale structural similarity for image quality assessment[C]\/\/The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, (2003). Ieee, 2003, 2: 1398-1402"},{"issue":"11","key":"1326_CR27","first-page":"2506","volume":"40","author":"Lin Suzhen","year":"2017","unstructured":"Suzhen, Lin, Ze, Han: Image fusion based on deep stacked convolutional neural network. J. Comput. Sci. 40(11), 2506\u20132518 (2017)","journal-title":"J. Comput. Sci."},{"issue":"1","key":"1326_CR28","first-page":"46","volume":"13","author":"J Yonghong","year":"2012","unstructured":"Yonghong, J.: Fusion of landsat TM and SAR images based on principal component analysis. Remote Sens. Technol. Appl. 13(1), 46\u201349 (2012)","journal-title":"Remote Sens. Technol. Appl."},{"issue":"18","key":"1326_CR29","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1049\/el:20081754","volume":"44","author":"M Hossny","year":"2008","unstructured":"Hossny, M., Nahavandi, S., Creighton, D.: Comments on\u2019Information measure for performance of image fusion. Electr. Lett. 44(18), 1066\u20131067 (2008)","journal-title":"Electr. Lett."},{"issue":"7","key":"1326_CR30","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. Electr. Lett. 38(7), 313\u2013315 (2002)","journal-title":"Electr. Lett."},{"issue":"2","key":"1326_CR31","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.inffus.2005.05.001","volume":"8","author":"V Petrovi\u0107","year":"2007","unstructured":"Petrovi\u0107, V.: Subjective tests for image fusion evaluation and objective metric validation. Inform. Fus. 8(2), 208\u2013216 (2007)","journal-title":"Inform. Fus."},{"issue":"1","key":"1326_CR32","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/TCI.2017.2786138","volume":"4","author":"K Ma","year":"2017","unstructured":"Ma, K., Duanmu, Z., Yeganeh, H., et al.: Multi-exposure image fusion by optimizing a structural similarity index. IEEE Trans. Comput. Imaging 4(1), 60\u201372 (2017)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"1326_CR33","doi-asserted-by":"crossref","unstructured":"Aslantas, V., Bendes, E.: A new image quality metric for image fusion: the sum of the correlations of differences. Aeu-international J. Electr. Commun. 69(12), 1890\u20131896 (2015)","DOI":"10.1016\/j.aeue.2015.09.004"},{"issue":"9","key":"1326_CR34","first-page":"10","volume":"73","author":"A Rana","year":"2013","unstructured":"Rana, A., Arora, S.: Comparative analysis of medical image fusion. Int. J. Comput. Appl. 73(9), 10\u201313 (2013)","journal-title":"Int. J. Comput. Appl."},{"issue":"4","key":"1326_CR35","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1049\/el:20000267","volume":"36","author":"CS Xydeas","year":"2000","unstructured":"Xydeas, C.S., Petrovic, V.: Objective image fusion performance measure. Electr. Lett. 36(4), 308-309 (2000)","journal-title":"Electr. Lett."},{"issue":"7","key":"1326_CR36","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1109\/TIP.2013.2253483","volume":"22","author":"S Li","year":"2013","unstructured":"Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Proc. 22(7), 2864\u20132875 (2013)","journal-title":"IEEE Trans. Image Proc."},{"key":"1326_CR37","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Maire, M., Belongie, S., et al.: Microsoft coco: Common objects in context[C]\/\/European conference on computer vision. Springer, Cham, (2014): 740-755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1326_CR38","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J .Y., et al.: Cbam: Convolutional block attention module[C]\/\/Proceedings of the European conference on computer vision (ECCV). (2018): 3-19","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1326_CR39","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., et al.: Gcnet: Non-local networks meet squeeze-excitation networks and beyond[C]\/\/Proceedings of the IEEE\/CVF international conference on computer vision workshops. (2019): 0-0","DOI":"10.1109\/ICCVW.2019.00246"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-022-01326-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-022-01326-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-022-01326-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T16:12:49Z","timestamp":1662999169000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-022-01326-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,10]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["1326"],"URL":"https:\/\/doi.org\/10.1007\/s00138-022-01326-6","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,10]]},"assertion":[{"value":"25 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"75"}}