{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T19:40:40Z","timestamp":1776282040536,"version":"3.50.1"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City","award":["2021JJLH0079"],"award-info":[{"award-number":["2021JJLH0079"]}]},{"name":"Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City","award":["2021CXLH0020"],"award-info":[{"award-number":["2021CXLH0020"]}]},{"name":"Innovational Fund for Scientific and Technological Personnel of Hainan Province","award":["KJRC2023D19"],"award-info":[{"award-number":["KJRC2023D19"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s00371-024-03637-3","type":"journal-article","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T13:01:34Z","timestamp":1728133294000},"page":"3883-3906","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["SwinMFF: toward high-fidelity end-to-end multi-focus image fusion via swin transformer-based network"],"prefix":"10.1007","volume":"41","author":[{"given":"Xinzhe","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Buyu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiliang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangyan","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sangjun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,5]]},"reference":[{"issue":"16","key":"3637_CR1","doi-asserted-by":"crossref","first-page":"3280","DOI":"10.1364\/AO.26.003280","volume":"26","author":"JW Bacus","year":"1987","unstructured":"Bacus, J.W., Grace, L.J.: Optical microscope system for standardized cell measurements and analyses. Appl. Opt. 26(16), 3280\u20133293 (1987)","journal-title":"Appl. Opt."},{"key":"3637_CR2","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.measurement.2019.04.034","volume":"141","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Deng, N., Xin, B.-J., Xing, W.-Y., Zhang, Z.-Y.: Structural characterization and measurement of nonwoven fabrics based on multi-focus image fusion. Measurement 141, 356\u2013363 (2019)","journal-title":"Measurement"},{"key":"3637_CR3","doi-asserted-by":"crossref","first-page":"3217","DOI":"10.1007\/s00170-019-03407-9","volume":"102","author":"L Juo\u010das","year":"2019","unstructured":"Juo\u010das, L., Raudonis, V., Maskeli\u016bnas, R., Dama\u0161evi\u010dius, R., Wo\u017aniak, M.: Multi-focusing algorithm for microscopy imagery in assembly line using low-cost camera. Int. J. Adv. Manuf. Technol. 102, 3217\u20133227 (2019)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"3637_CR4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.sigpro.2019.03.020","volume":"161","author":"Y Liang","year":"2019","unstructured":"Liang, Y., Mao, Y., Tang, Z., Yan, M., Zhao, Y., Liu, J.: Efficient misalignment-robust multi-focus microscopical images fusion. Signal Process. 161, 111\u2013123 (2019)","journal-title":"Signal Process."},{"key":"3637_CR5","doi-asserted-by":"crossref","unstructured":"Li, X., Li, X., Tan, H., Li, J.: Samf: small-area-aware multi-focus image fusion for object detection. In: ICASSP 2024\u20132024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3845\u20133849. IEEE (2024)","DOI":"10.1109\/ICASSP48485.2024.10447642"},{"key":"3637_CR6","doi-asserted-by":"publisher","unstructured":"Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. In: Fischler MA, Firschein O (eds) Readings in Computer Vision, pp. 671\u2013679. Morgan Kaufmann, Elsevier (1987). https:\/\/doi.org\/10.1016\/B978-0-08-051581-6","DOI":"10.1016\/B978-0-08-051581-6"},{"key":"3637_CR7","doi-asserted-by":"crossref","unstructured":"Burt, P.J., Kolczynski, R.J.: Enhanced image capture through fusion. In: 1993 (4th) International Conference on Computer Vision, pp. 173\u2013182. IEEE (1993)","DOI":"10.1109\/ICCV.1993.378222"},{"issue":"2","key":"3637_CR8","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.inffus.2005.09.006","volume":"8","author":"JJ Lewis","year":"2007","unstructured":"Lewis, J.J., O\u2019Callaghan, R.J., Nikolov, S.G., Bull, D.R., Canagarajah, N.: Pixel-and region-based image fusion with complex wavelets. Inf Fusion 8(2), 119\u2013130 (2007)","journal-title":"Inf Fusion"},{"issue":"3","key":"3637_CR9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1006\/gmip.1995.1022","volume":"57","author":"H Li","year":"1995","unstructured":"Li, H., Manjunath, B., Mitra, S.K.: Multisensor image fusion using the wavelet transform. Graph. Models Image Process. 57(3), 235\u2013245 (1995)","journal-title":"Graph. Models Image Process."},{"key":"3637_CR10","doi-asserted-by":"crossref","unstructured":"Yang, B., Li, S., Sun, F.: Image fusion using nonsubsampled contourlet transform. In: Fourth International Conference on Image and Graphics (ICIG 2007), pp. 719\u2013724. IEEE (2007)","DOI":"10.1109\/ICIG.2007.124"},{"issue":"7","key":"3637_CR11","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1016\/j.sigpro.2009.01.012","volume":"89","author":"Q Zhang","year":"2009","unstructured":"Zhang, Q., Guo, B.: Multifocus image fusion using the nonsubsampled contourlet transform. Signal Process. 89(7), 1334\u20131346 (2009)","journal-title":"Signal Process."},{"key":"3637_CR12","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.inffus.2016.09.007","volume":"35","author":"Z Liu","year":"2017","unstructured":"Liu, Z., Chai, Y., Yin, H., Zhou, J., Zhu, Z.: A novel multi-focus image fusion approach based on image decomposition. Inf. Fusion 35, 102\u2013116 (2017)","journal-title":"Inf. Fusion"},{"key":"3637_CR13","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.inffus.2013.06.001","volume":"18","author":"Y Jiang","year":"2014","unstructured":"Jiang, Y., Wang, M.: Image fusion with morphological component analysis. Inf. Fusion 18, 107\u2013118 (2014)","journal-title":"Inf. Fusion"},{"key":"3637_CR14","first-page":"1","volume":"70","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Wang, L., Cheng, J., Chen, X.: Multiscale feature interactive network for multifocus image fusion. IEEE Trans. Instrum. Meas. 70, 1\u201316 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3637_CR15","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.inffus.2022.11.014","volume":"92","author":"X Hu","year":"2023","unstructured":"Hu, X., Jiang, J., Liu, X., Ma, J.: Zmff: zero-shot multi-focus image fusion. Inf. Fusion 92, 127\u2013138 (2023)","journal-title":"Inf. Fusion"},{"key":"3637_CR16","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1007\/s11042-016-4312-3","volume":"77","author":"K Sujatha","year":"2018","unstructured":"Sujatha, K., Shalini Punithavathani, D.: Optimized ensemble decision-based multi-focus imagefusion using binary genetic grey-wolf optimizer in camera sensor networks. Multimed. Tools Appl. 77, 1735\u20131759 (2018)","journal-title":"Multimed. Tools Appl."},{"key":"3637_CR17","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.compeleceng.2016.01.013","volume":"54","author":"N Kausar","year":"2016","unstructured":"Kausar, N., Majid, A., Javed, S.G.: A novel ensemble approach using individual features for multi-focus image fusion. Comput. Electr. Eng. 54, 393\u2013405 (2016)","journal-title":"Comput. Electr. Eng."},{"key":"3637_CR18","doi-asserted-by":"crossref","first-page":"47082","DOI":"10.1109\/ACCESS.2018.2866867","volume":"6","author":"Y Huang","year":"2018","unstructured":"Huang, Y., Li, W., Gao, M., Liu, Z.: Algebraic multi-grid based multi-focus image fusion using watershed algorithm. IEEE Access 6, 47082\u201347091 (2018)","journal-title":"IEEE Access"},{"key":"3637_CR19","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.neucom.2018.08.024","volume":"318","author":"J Duan","year":"2018","unstructured":"Duan, J., Chen, L., Chen, C.P.: Multifocus image fusion with enhanced linear spectral clustering and fast depth map estimation. Neurocomputing 318, 43\u201354 (2018)","journal-title":"Neurocomputing"},{"key":"3637_CR20","doi-asserted-by":"publisher","first-page":"4353","DOI":"10.1007\/s00371-021-02300-5","volume":"38","author":"NS Jagtap","year":"2022","unstructured":"Jagtap, N.S., Thepade, S.D.: High-quality image multi-focus fusion to address ringing and blurring artifacts without loss of information. Vis. Comput. 38, 4353\u20134371 (2022). https:\/\/doi.org\/10.1007\/s00371-021-02300-5","journal-title":"Vis. Comput."},{"key":"3637_CR21","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.asoc.2016.11.033","volume":"51","author":"W Kong","year":"2017","unstructured":"Kong, W., Lei, Y.: Multi-focus image fusion using biochemical ion exchange model. Appl. Soft Comput. 51, 314\u2013327 (2017)","journal-title":"Appl. Soft Comput."},{"key":"3637_CR22","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121156","volume":"235","author":"Z Duan","year":"2024","unstructured":"Duan, Z., Luo, X., Zhang, T.: Combining transformers with CNN for multi-focus image fusion. Expert Syst. Appl. 235, 121156 (2024)","journal-title":"Expert Syst. Appl."},{"key":"3637_CR23","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1109\/TIP.2020.2976190","volume":"29","author":"J Li","year":"2020","unstructured":"Li, J., Guo, X., Lu, G., Zhang, B., Xu, Y., Wu, F., Zhang, D.: Drpl: deep regression pair learning for multi-focus image fusion. IEEE Trans. Image Process. 29, 4816\u20134831 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3637_CR24","doi-asserted-by":"publisher","unstructured":"Li, X., Li, X., Cheng, X., Wang, M., Tan, H.: MCDFD: multifocus image fusion based on multiscale cross-difference and focus detection. IEEE Sens. J. 23(24), 30913\u201330926 (2023). https:\/\/doi.org\/10.1109\/JSEN.2023.3330871","DOI":"10.1109\/JSEN.2023.3330871"},{"key":"3637_CR25","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-97-5208-9","volume":"105","author":"J Wang","year":"2024","unstructured":"Wang, J., Qu, H., Zhang, Z., Xie, M.: New insights into multi-focus image fusion: a fusion method based on multi-dictionary linear sparse representation and region fusion model. Inf. Fusion 105, 102230 (2024)","journal-title":"Inf. Fusion"},{"key":"3637_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03351-0","author":"Y Hu","year":"2024","unstructured":"Hu, Y., Wu, P., Zhang, B., et al.: A new multi-focus image fusion quality assessment method with convolutional sparse representation. Vis. Comput. (2024). https:\/\/doi.org\/10.1007\/s00371-024-03351-0","journal-title":"Vis. Comput."},{"key":"3637_CR27","doi-asserted-by":"crossref","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., Wang, Z.: Multi-focus image fusion with a deep convolutional neural network. Inf. Fusion 36, 191\u2013207 (2017)","journal-title":"Inf. Fusion"},{"key":"3637_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"7","key":"3637_CR29","doi-asserted-by":"crossref","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. Sin. 9(7), 1200\u20131217 (2022)","journal-title":"IEEE\/CAA J. Autom. Sin."},{"issue":"8","key":"3637_CR30","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1109\/TMM.2019.2895292","volume":"21","author":"X Guo","year":"2019","unstructured":"Guo, X., Nie, R., Cao, J., Zhou, D., Mei, L., He, K.: Fusegan: learning to fuse multi-focus image via conditional generative adversarial network. IEEE Trans. Multimed. 21(8), 1982\u20131996 (2019)","journal-title":"IEEE Trans. Multimed."},{"key":"3637_CR31","volume":"238","author":"M Li","year":"2024","unstructured":"Li, M., Pei, R., Zheng, T., Zhang, Y., Fu, W.: Fusiondiff: multi-focus image fusion using denoising diffusion probabilistic models. Expert Syst. Appl. 238, 121664 (2024)","journal-title":"Expert Syst. Appl."},{"key":"3637_CR32","doi-asserted-by":"crossref","first-page":"114385","DOI":"10.1109\/ACCESS.2019.2935006","volume":"7","author":"R Lai","year":"2019","unstructured":"Lai, R., Li, Y., Guan, J., Xiong, A.: Multi-scale visual attention deep convolutional neural network for multi-focus image fusion. IEEE Access 7, 114385\u2013114399 (2019)","journal-title":"IEEE Access"},{"key":"3637_CR33","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.neucom.2021.10.115","volume":"470","author":"B Ma","year":"2022","unstructured":"Ma, B., Yin, X., Wu, D., Shen, H., Ban, X., Wang, Y.: End-to-end learning for simultaneously generating decision map and multi-focus image fusion result. Neurocomputing 470, 204\u2013216 (2022)","journal-title":"Neurocomputing"},{"key":"3637_CR34","first-page":"1","volume":"70","author":"Y Zang","year":"2021","unstructured":"Zang, Y., Zhou, D., Wang, C., Nie, R., Guo, Y.: UFA-FUSE: a novel deep supervised and hybrid model for multifocus image fusion. IEEE Trans. Instrum. Meas. 70, 1\u201317 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3637_CR35","doi-asserted-by":"crossref","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., Xu, H., Ma, J.: MFF-GAN: an unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion. Inf. Fusion 66, 40\u201353 (2021)","journal-title":"Inf. Fusion"},{"key":"3637_CR36","doi-asserted-by":"publisher","unstructured":"Liu, Y., Li, X., Liu Y., Zhong, W.: Simplifusion: a simplified infrared and visible image fusion network. Vis. Comput. 1\u201316 (2024). https:\/\/doi.org\/10.1007\/s00371-024-03423-1","DOI":"10.1007\/s00371-024-03423-1"},{"key":"3637_CR37","doi-asserted-by":"crossref","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., Yan, H., Zhao, X., Zhang, L.: IFCNN: a general image fusion framework based on convolutional neural network. Inf. Fusion 54, 99\u2013118 (2020)","journal-title":"Inf. Fusion"},{"issue":"1","key":"3637_CR38","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","volume":"44","author":"H Xu","year":"2020","unstructured":"Xu, H., Ma, J., Jiang, J., Guo, X., Ling, H.: 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":"3637_CR39","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/TIP.2020.3033158","volume":"30","author":"B Xiao","year":"2020","unstructured":"Xiao, B., Xu, B., Bi, X., Li, W.: Global-feature encoding U-Net (GEU-Net) for multi-focus image fusion. IEEE Trans. Image Process. 30, 163\u2013175 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3637_CR40","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.inffus.2019.02.003","volume":"51","author":"M Amin-Naji","year":"2019","unstructured":"Amin-Naji, M., Aghagolzadeh, A., Ezoji, M.: Ensemble of CNN for multi-focus image fusion. Inf. Fusion 51, 201\u2013214 (2019)","journal-title":"Inf. Fusion"},{"issue":"14","key":"3637_CR41","doi-asserted-by":"crossref","first-page":"15883","DOI":"10.1007\/s10489-022-03194-z","volume":"52","author":"Z Guan","year":"2022","unstructured":"Guan, Z., Wang, X., Nie, R., Yu, S., Wang, C.: NCDCN: multi-focus image fusion via nest connection and dilated convolution network. Appl. Intell. 52(14), 15883\u201315898 (2022)","journal-title":"Appl. Intell."},{"key":"3637_CR42","doi-asserted-by":"crossref","first-page":"5793","DOI":"10.1007\/s00521-020-05358-9","volume":"33","author":"B Ma","year":"2021","unstructured":"Ma, B., Zhu, Y., Yin, X., Ban, X., Huang, H., Mukeshimana, M.: Sesf-fuse: an unsupervised deep model for multi-focus image fusion. Neural Comput. Appl. 33, 5793\u20135804 (2021)","journal-title":"Neural Comput. Appl."},{"key":"3637_CR43","volume":"96","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Xu, S., Liu, J., Zhao, Z., Zhang, C., Zhang, J.: MFIF-GAN: a new generative adversarial network for multi-focus image fusion. Signal Process. Image Commun. 96, 116295 (2021)","journal-title":"Signal Process. Image Commun."},{"key":"3637_CR44","doi-asserted-by":"publisher","first-page":"3950","DOI":"10.1109\/TIP.2024.3409940","volume":"33","author":"X Hu","year":"2024","unstructured":"Hu, X., Jiang, J., Wang, C., Liu, X., Ma, J.: Incrementally adapting pretrained model using network prior for multi-focus image fusion. IEEE Trans. Image Process. 33, 3950\u20133963 (2024). https:\/\/doi.org\/10.1109\/TIP.2024.3409940","journal-title":"IEEE Trans. Image Process."},{"key":"3637_CR45","doi-asserted-by":"crossref","first-page":"7192","DOI":"10.1109\/TIP.2020.2999854","volume":"29","author":"A Nazir","year":"2020","unstructured":"Nazir, A., Cheema, M.N., Sheng, B., Li, H., Li, P., Yang, P., Jung, Y., Qin, J., Kim, J., Feng, D.D.: OFF-ENET: an optimally fused fully end-to-end network for automatic dense volumetric 3d intracranial blood vessels segmentation. IEEE Trans. Image Process. 29, 7192\u20137202 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3637_CR46","doi-asserted-by":"crossref","unstructured":"Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., Bry, A.: End-to-end learning of geometry and context for deep stereo regression. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 66\u201375 (2017)","DOI":"10.1109\/ICCV.2017.17"},{"issue":"10","key":"3637_CR47","doi-asserted-by":"crossref","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(10), 2761\u20132785 (2021)","journal-title":"Int. J. Comput. Vis."},{"key":"3637_CR48","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141, Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000\u20136010 (2017)"},{"key":"3637_CR49","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"3637_CR50","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/TMM.2021.3120873","volume":"25","author":"X Lin","year":"2021","unstructured":"Lin, X., Sun, S., Huang, W., Sheng, B., Li, P., Feng, D.D.: EAPT: efficient attention pyramid transformer for image processing. IEEE Trans. Multimed. 25, 50\u201361 (2021)","journal-title":"IEEE Trans. Multimed."},{"key":"3637_CR51","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van\u00a0Gool, L., Timofte, R.: Swinir: Image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"3637_CR52","doi-asserted-by":"crossref","unstructured":"Vs, V., Valanarasu, J.M.J., Oza, P., Patel, V.M.: Image fusion transformer. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 3566\u20133570 (2022). IEEE","DOI":"10.1109\/ICIP46576.2022.9897280"},{"key":"3637_CR53","doi-asserted-by":"crossref","unstructured":"Qu, L., Liu, S., Wang, M., Song, Z.: Transmef: A transformer-based multi-exposure image fusion framework using self-supervised multi-task learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2126\u20132134 (2022)","DOI":"10.1609\/aaai.v36i2.20109"},{"key":"3637_CR54","doi-asserted-by":"crossref","unstructured":"Ram\u00a0Prabhakar, K., Sai\u00a0Srikar, V., Venkatesh\u00a0Babu, R.: Deepfuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4714\u20134722 (2017)","DOI":"10.1109\/ICCV.2017.505"},{"issue":"5","key":"3637_CR55","doi-asserted-by":"crossref","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(5), 2614\u20132623 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"3637_CR56","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, H., Wang, Y., Feng, M., Wang, D., Yin, B., Ruan, X.: Learning to detect salient objects with image-level supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 136\u2013145 (2017)","DOI":"10.1109\/CVPR.2017.404"},{"key":"3637_CR57","doi-asserted-by":"crossref","unstructured":"Rockinger, O.: Image sequence fusion using a shift-invariant wavelet transform. In: Proceedings of International Conference on Image Processing, vol. 3, pp. 288\u2013291. IEEE (1997)","DOI":"10.1109\/ICIP.1997.632093"},{"issue":"7","key":"3637_CR58","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1109\/TIP.2013.2244222","volume":"22","author":"S Li","year":"2013","unstructured":"Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. 22(7), 2864\u20132875 (2013)","journal-title":"IEEE Trans. Image Process."},{"issue":"6583","key":"3637_CR59","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1038\/381607a0","volume":"381","author":"BA Olshausen","year":"1996","unstructured":"Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607\u2013609 (1996)","journal-title":"Nature"},{"issue":"5","key":"3637_CR60","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1049\/iet-ipr.2014.0311","volume":"9","author":"Y Liu","year":"2015","unstructured":"Liu, Y., Wang, Z.: Simultaneous image fusion and denoising with adaptive sparse representation. IET Image Proc. 9(5), 347\u2013357 (2015)","journal-title":"IET Image Proc."},{"key":"3637_CR61","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.inffus.2013.11.005","volume":"20","author":"Z Zhou","year":"2014","unstructured":"Zhou, Z., Li, S., Wang, B.: Multi-scale weighted gradient-based fusion for multi-focus images. Inf. Fusion 20, 60\u201372 (2014)","journal-title":"Inf. Fusion"},{"issue":"10","key":"3637_CR62","doi-asserted-by":"crossref","first-page":"1650123","DOI":"10.1142\/S0218126616501231","volume":"25","author":"S Paul","year":"2016","unstructured":"Paul, S., Sevcenco, I.S., Agathoklis, P.: Multi-exposure and multi-focus image fusion in gradient domain. J. Circuits Syst. Comput. 25(10), 1650123 (2016)","journal-title":"J. Circuits Syst. Comput."},{"key":"3637_CR63","doi-asserted-by":"crossref","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. Fusion 24, 147\u2013164 (2015)","journal-title":"Inf. Fusion"},{"key":"3637_CR64","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.optcom.2014.10.031","volume":"338","author":"D Guo","year":"2015","unstructured":"Guo, D., Yan, J., Qu, X.: High quality multi-focus image fusion using self-similarity and depth information. Opt. Commun. 338, 138\u2013144 (2015)","journal-title":"Opt. Commun."},{"issue":"2","key":"3637_CR65","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.inffus.2012.01.007","volume":"14","author":"I De","year":"2013","unstructured":"De, I., Chanda, B.: Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure. Inf. Fusion 14(2), 136\u2013146 (2013)","journal-title":"Inf. Fusion"},{"key":"3637_CR66","doi-asserted-by":"crossref","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. Inf. Fusion 25, 72\u201384 (2015)","journal-title":"Inf. Fusion"},{"key":"3637_CR67","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.image.2018.12.004","volume":"72","author":"X Qiu","year":"2019","unstructured":"Qiu, X., Li, M., Zhang, L., Yuan, X.: Guided filter-based multi-focus image fusion through focus region detection. Signal Process. Image Commun. 72, 35\u201346 (2019)","journal-title":"Signal Process. Image Commun."},{"key":"3637_CR68","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.neucom.2019.01.048","volume":"335","author":"J Ma","year":"2019","unstructured":"Ma, J., Zhou, Z., Wang, B., Miao, L., Zong, H.: Multi-focus image fusion using boosted random walks-based algorithm with two-scale focus maps. Neurocomputing 335, 9\u201320 (2019)","journal-title":"Neurocomputing"},{"issue":"2","key":"3637_CR69","doi-asserted-by":"crossref","first-page":"023027","DOI":"10.1117\/1.JEI.28.2.023027","volume":"28","author":"K Zhan","year":"2019","unstructured":"Zhan, K., Kong, L., Liu, B., He, Y.: Multimodal image seamless fusion. J. Electron. Imaging 28(2), 023027\u2013023027 (2019)","journal-title":"J. Electron. Imaging"},{"issue":"9","key":"3637_CR70","doi-asserted-by":"publisher","first-page":"4819","DOI":"10.1109\/TPAMI.2021.3078906","volume":"44","author":"X Zhang","year":"2022","unstructured":"Zhang, X.: Deep learning-based multi-focus image fusion: a survey and a comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 4819\u20134838 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3078906","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3637_CR71","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, L., Cheng, J., Li, C., Chen, X.: Multi-focus image fusion: a survey of the state of the art. Inf. Fusion 64, 71\u201391 (2020)","DOI":"10.1016\/j.inffus.2020.06.013"},{"key":"3637_CR72","unstructured":"Xu, S., Wei, X., Zhang, C., Liu, J., Zhang, J.: Mffw: A new dataset for multi-focus image fusion. arXiv preprint arXiv:2002.04780 (2020)"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03637-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03637-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03637-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T10:22:21Z","timestamp":1744798941000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03637-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,5]]},"references-count":72,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["3637"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03637-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,5]]},"assertion":[{"value":"1 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}