{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:33:26Z","timestamp":1772120006767,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:00:00Z","timestamp":1746230400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:00:00Z","timestamp":1746230400000},"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":[[2025,6]]},"DOI":"10.1007\/s11760-025-04131-6","type":"journal-article","created":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T04:44:27Z","timestamp":1746247467000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Infrared and visible image fusion network based on low-light image enhancement and attention mechanism"],"prefix":"10.1007","volume":"19","author":[{"given":"Jinbo","family":"Lu","sequence":"first","affiliation":[]},{"given":"Zhen","family":"Pei","sequence":"additional","affiliation":[]},{"given":"Jinling","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Kunyu","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Ran","sequence":"additional","affiliation":[]},{"given":"Hongyan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,3]]},"reference":[{"key":"4131_CR1","doi-asserted-by":"crossref","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., Jiang, J., Ma, J.: Image fusion meets deep learning: a survey and perspective. Inf. Fusion 76, 323\u2013336 (2021)","journal-title":"Inf. Fusion"},{"issue":"6","key":"4131_CR2","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.1109\/TCSVT.2021.3109895","volume":"32","author":"Z Wang","year":"2021","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. Circuits Syst. Video Technol. 32(6), 3360\u20133374 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"2","key":"4131_CR3","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.inffus.2008.08.008","volume":"10","author":"AC Muller","year":"2009","unstructured":"Muller, A.C., Narayanan, S.: Cognitively-engineered multisensor image fusion for military applications. Inf. Fusion 10(2), 137\u2013149 (2009)","journal-title":"Inf. Fusion"},{"key":"4131_CR4","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.inffus.2016.03.003","volume":"32","author":"H Ghassemian","year":"2016","unstructured":"Ghassemian, H.: A review of remote sensing image fusion methods. Inf. Fusion 32, 75\u201389 (2016)","journal-title":"Inf. Fusion"},{"issue":"1","key":"4131_CR5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.biosystemseng.2009.02.009","volume":"103","author":"DM Bulanon","year":"2009","unstructured":"Bulanon, D.M., Burks, T.F., Alchanatis, V.: Image fusion of visible and thermal images for fruit detection. Biosys. Eng. 103(1), 12\u201322 (2009)","journal-title":"Biosys. Eng."},{"issue":"1","key":"4131_CR6","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.cviu.2004.04.001","volume":"97","author":"SG Kong","year":"2005","unstructured":"Kong, S.G., Heo, J., Abidi, B.R., Paik, J., Abidi, M.A.: Recent advances in visual and infrared face recognition\u2014a review. Comput. Vis. Image Underst. 97(1), 103\u2013135 (2005)","journal-title":"Comput. Vis. Image Underst."},{"key":"4131_CR7","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.inffus.2021.02.008","volume":"71","author":"G Li","year":"2021","unstructured":"Li, G., Lin, Y., Qu, X.: An infrared and visible image fusion method based on multi-scale transformation and norm optimization. Information Fusion 71, 109\u2013129 (2021)","journal-title":"Information Fusion"},{"issue":"9","key":"4131_CR8","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1049\/iet-ipr.2019.0322","volume":"14","author":"AM Sharma","year":"2020","unstructured":"Sharma, A.M., Dogra, A., Goyal, B., Vig, R., Agrawal, S.: From pyramids to state-of-the-art: a study and comprehensive comparison of visible\u2013infrared image fusion techniques. IET Image Proc. 14(9), 1671\u20131689 (2020)","journal-title":"IET Image Proc."},{"issue":"1","key":"4131_CR9","doi-asserted-by":"crossref","first-page":"275138","DOI":"10.1155\/2012\/275138","volume":"2012","author":"Y Niu","year":"2012","unstructured":"Niu, Y., Xu, S., Wu, L., Hu, W.: Airborne infrared and visible image fusion for target perception based on target region segmentation and discrete wavelet transform. Math. Probl. Eng. 2012(1), 275138 (2012)","journal-title":"Math. Probl. Eng."},{"key":"4131_CR10","doi-asserted-by":"crossref","unstructured":"Yin, S., Cao, L., Tan, Q., Jin, G.: Infrared and visible image fusion based on NSCT and fuzzy logic. In: 2010 IEEE International Conference on Mechatronics and Automation, (2010)","DOI":"10.1109\/ICMA.2010.5588318"},{"issue":"3","key":"4131_CR11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1360612.1360666","volume":"27","author":"Z Farbman","year":"2008","unstructured":"Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transa. Grap. (TOG) 27(3), 1\u201310 (2008)","journal-title":"ACM Transa. Grap. (TOG)"},{"key":"4131_CR12","doi-asserted-by":"crossref","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., Han, J., Tao, D.: Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review. Inf. Fusion 40, 57\u201375 (2018)","journal-title":"Inf. Fusion"},{"key":"4131_CR13","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","volume":"45","author":"J Ma","year":"2019","unstructured":"Ma, J., Ma, Y., Li, C.: Infrared and visible image fusion methods and applications: a survey. Inf. fusion 45, 153\u2013178 (2019)","journal-title":"Inf. fusion"},{"key":"4131_CR14","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.infrared.2015.10.004","volume":"73","author":"B Zhang","year":"2015","unstructured":"Zhang, B., Lu, X., Pei, H., Zhao, Y.: A fusion algorithm for infrared and visible images based on saliency analysis and non-subsampled Shearlet transform. Infrared Phys. Technol. 73, 286\u2013297 (2015)","journal-title":"Infrared Phys. Technol."},{"key":"4131_CR15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.infrared.2015.07.003","volume":"72","author":"W Gan","year":"2015","unstructured":"Gan, W., Wu, X., Wu, W., Yang, X., Ren, C., He, X., Liu, K.: Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter. Infrared Phys. Technol. 72, 37\u201351 (2015)","journal-title":"Infrared Phys. Technol."},{"key":"4131_CR16","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.02.001","volume":"31","author":"J Ma","year":"2016","unstructured":"Ma, J., Chen, C., Li, C., Huang, J.: Infrared and visible image fusion via gradient transfer and total variation minimization. Inf. Fusion 31, 100\u2013109 (2016)","journal-title":"Inf. Fusion"},{"key":"4131_CR17","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.infrared.2017.01.012","volume":"81","author":"J Zhao","year":"2017","unstructured":"Zhao, J., Cui, G., Gong, X., Zang, Y., Tao, S., Wang, D.: Fusion of visible and infrared images using global entropy and gradient constrained regularization. Infrared Phys. Technol. 81, 201\u2013209 (2017)","journal-title":"Infrared Phys. Technol."},{"key":"4131_CR18","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2012.12.015","volume":"111","author":"J Han","year":"2013","unstructured":"Han, J., Pauwels, E.J., De Zeeuw, P.: Fast saliency-aware multi-modality image fusion. Neurocomputing 111, 70\u201380 (2013)","journal-title":"Neurocomputing"},{"key":"4131_CR19","doi-asserted-by":"crossref","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. Fusion 48, 11\u201326 (2019)","journal-title":"Inf. Fusion"},{"key":"4131_CR20","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TCI.2020.2965304","volume":"6","author":"R Hou","year":"2020","unstructured":"Hou, R., Zhou, D., Nie, R., Liu, D., Xiong, L., Guo, Y., Yu, C.: VIF-Net: An unsupervised framework for infrared and visible image fusion. IEEE Transa. Comput. Imag. 6, 640\u2013651 (2020)","journal-title":"IEEE Transa. Comput. Imag."},{"issue":"3","key":"4131_CR21","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1109\/TCSVT.2021.3075745","volume":"32","author":"Z Zhao","year":"2021","unstructured":"Zhao, Z., Xu, S., Zhang, J., Liang, C., Zhang, C., Liu, J.: Efficient and model-based infrared and visible image fusion via algorithm unrolling. IEEE Trans. Circuits Syst. Video Technol. 32(3), 1186\u20131196 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4131_CR22","doi-asserted-by":"crossref","unstructured":"Fu, X., Zeng, D., Huang, Y., Zhang, X. P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, (2016)","DOI":"10.1109\/CVPR.2016.304"},{"key":"4131_CR23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","volume":"83","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. Fusion 83, 79\u201392 (2022)","journal-title":"Inf. Fusion"},{"issue":"6","key":"4131_CR24","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1016\/j.sigpro.2012.09.009","volume":"93","author":"KV Arya","year":"2013","unstructured":"Arya, K.V., Pattanaik, M.: Histogram statistics based variance controlled adaptive threshold in anisotropic diffusion for low contrast image enhancement. Signal Process. 93(6), 1684\u20131693 (2013)","journal-title":"Signal Process."},{"key":"4131_CR25","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.sigpro.2016.05.031","volume":"129","author":"X Fu","year":"2016","unstructured":"Fu, X., Zeng, D., Huang, Y., Liao, Y., Ding, X., Paisley, J.: A fusion-based enhancing method for weakly illuminated images. Signal Process. 129, 82\u201396 (2016)","journal-title":"Signal Process."},{"key":"4131_CR26","doi-asserted-by":"crossref","unstructured":"Tao, L., Zhu, C., Song, J., Lu, T., Jia, H., & Xie, X.: Low-light image enhancement using CNN and bright channel prior. In: 2017 IEEE International Conference on Image Processing (ICIP), (2017)","DOI":"10.1109\/ICIP.2017.8296876"},{"issue":"7","key":"4131_CR27","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1007\/s11263-021-01466-8","volume":"129","author":"F Lv","year":"2021","unstructured":"Lv, F., Li, Y., Lu, F.: Attention guided low-light image enhancement with a large scale low-light simulation dataset. Int. J. Comput. Vision 129(7), 2175\u20132193 (2021)","journal-title":"Int. J. Comput. Vision"},{"issue":"2","key":"4131_CR28","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","volume":"26","author":"X Guo","year":"2016","unstructured":"Guo, X., Li, Y., Ling, H.: LIME: Low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982\u2013993 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"4131_CR29","doi-asserted-by":"crossref","first-page":"865820","DOI":"10.3389\/fbioe.2022.865820","volume":"10","author":"Y Sun","year":"2022","unstructured":"Sun, Y., Zhao, Z., Jiang, D., Tong, X., Tao, B., Jiang, G., Fang, Z.: Low-illumination image enhancement algorithm based on improved multi-scale Retinex and ABC algorithm optimization. Front. Bioeng. Biotechnol. 10, 865820 (2022)","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"5","key":"4131_CR30","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":"4131_CR31","doi-asserted-by":"crossref","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. Fusion 73, 72\u201386 (2021)","journal-title":"Inf. Fusion"},{"key":"4131_CR32","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.1109\/LSP.2021.3109818","volume":"28","author":"J Liu","year":"2021","unstructured":"Liu, J., Wu, Y., Huang, Z., Liu, R., Fan, X.: Smoa: searching a modality-oriented architecture for infrared and visible image fusion. IEEE Signal Process. Lett. 28, 1818\u20131822 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"4131_CR33","first-page":"1","volume":"71","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Wu, Y., Wang, J., Xu, J., Shao, W.: Res2Fusion: infrared and visible image fusion based on dense Res2net and double nonlocal attention models. IEEE Trans. Instrum. Meas. 71, 1\u201312 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"12","key":"4131_CR34","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1109\/TIM.2020.3005230","volume":"69","author":"H Li","year":"2020","unstructured":"Li, H., Wu, X.J., Durrani, T.: NestFuse: an infrared and visible image fusion architecture based on nest connection and spatial\/channel attention models. IEEE Trans. Instrum. Meas. 69(12), 9645\u20139656 (2020)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4131_CR35","first-page":"1","volume":"72","author":"H Li","year":"2023","unstructured":"Li, H., Xiao, Y., Cheng, C., Shen, Z., Song, X.: DePF: a novel fusion approach based on decomposition pooling for infrared and visible images. Transa. Instrument. Meas. 72, 1\u20134 (2023)","journal-title":"Transa. Instrument. Meas."},{"issue":"6","key":"4131_CR36","first-page":"1","volume":"31","author":"L Xu","year":"2012","unstructured":"Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Transa. Graph. (TOG) 31(6), 1\u201310 (2012)","journal-title":"ACM Transa. Graph. (TOG)"},{"issue":"6","key":"4131_CR37","doi-asserted-by":"crossref","first-page":"820","DOI":"10.3390\/s16060820","volume":"16","author":"A Gonz\u00e1lez","year":"2016","unstructured":"Gonz\u00e1lez, A., Fang, Z., Socarras, Y., Serrat, J., V\u00e1zquez, D., Xu, J., L\u00f3pez, A.M.: Pedestrian detection at day\/night time with visible and FIR cameras: A comparison. Sensors 16(6), 820 (2016)","journal-title":"Sensors"},{"issue":"9","key":"4131_CR38","doi-asserted-by":"crossref","first-page":"8808","DOI":"10.1109\/JSEN.2022.3161733","volume":"22","author":"D Zhu","year":"2022","unstructured":"Zhu, D., Zhan, W., Jiang, Y., Xu, X., Guo, R.: IPLF: A novel image pair learning fusion network for infrared and visible image. IEEE Sens. J. 22(9), 8808\u20138817 (2022)","journal-title":"IEEE Sens. J."},{"issue":"6","key":"4131_CR39","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1109\/TSMC.1978.4309999","volume":"8","author":"H Tamura","year":"1978","unstructured":"Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8(6), 460\u2013473 (1978)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"4131_CR40","first-page":"52","volume":"2","author":"B Rajalingam","year":"2018","unstructured":"Rajalingam, B., Priya, R.: Hybrid multimodality medical image fusion technique for feature enhancement in medical diagnosis. Int. J. Eng. Sci. Invent. 2, 52\u201360 (2018)","journal-title":"Int. J. Eng. Sci. Invent."},{"issue":"12","key":"4131_CR41","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1109\/26.477498","volume":"43","author":"AM Eskicioglu","year":"1995","unstructured":"Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Commun. 43(12), 2959\u20132965 (1995)","journal-title":"IEEE Trans. Commun."},{"issue":"11","key":"4131_CR42","doi-asserted-by":"crossref","first-page":"3345","DOI":"10.1109\/TIP.2015.2442920","volume":"24","author":"K Ma","year":"2015","unstructured":"Ma, K., Zeng, K., Wang, Z.: Perceptual quality assessment for multi-exposure image fusion. IEEE Trans. Image Process. 24(11), 3345\u20133356 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"4131_CR43","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.inffus.2011.08.002","volume":"14","author":"Y Han","year":"2013","unstructured":"Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Inf. Fusion 14(2), 127\u2013135 (2013)","journal-title":"Inf. Fusion"},{"issue":"5","key":"4131_CR44","first-page":"484","volume":"4","author":"M Deshmukh","year":"2010","unstructured":"Deshmukh, M., Bhosale, U.: Image fusion and image quality assessment of fused images. Int. J. Image Proc. (IJIP) 4(5), 484 (2010)","journal-title":"Int. J. Image Proc. (IJIP)"},{"issue":"1","key":"4131_CR45","doi-asserted-by":"crossref","first-page":"023522","DOI":"10.1117\/1.2945910","volume":"2","author":"JW Roberts","year":"2008","unstructured":"Roberts, J.W., Van Aardt, J.A., Ahmed, F.B.: Assessment of image fusion procedures using entropy, image quality, and multispectral classification. J. Appl. Remote Sens. 2(1), 023522 (2008)","journal-title":"J. Appl. Remote Sens."},{"issue":"7","key":"4131_CR46","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. Automatica Sinica 9(7), 1200\u20131217 (2022)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"4131_CR47","doi-asserted-by":"crossref","unstructured":"Jia, X., Zhu, C., Li, M., Tang, W., & Zhou, W.: LLVIP: A visible-infrared paired dataset for low-light vision. In: Proceedings of the IEEE\/CVF international conference on computer vision, (2021)","DOI":"10.1109\/ICCVW54120.2021.00389"},{"key":"4131_CR48","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.dib.2017.09.038","volume":"15","author":"A Toet","year":"2017","unstructured":"Toet, A.: The TNO multiband image data collection. Data Brief 15, 249\u2013251 (2017)","journal-title":"Data Brief"},{"key":"4131_CR49","doi-asserted-by":"crossref","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. Fusion 82, 28\u201342 (2022)","journal-title":"Inf. Fusion"},{"key":"4131_CR50","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.inffus.2022.11.010","volume":"92","author":"C Cheng","year":"2023","unstructured":"Cheng, C., Xu, T., Wu, X.J.: MUFusion: a general unsupervised image fusion network based on memory unit. Inf. Fusion 92, 80\u201392 (2023)","journal-title":"Inf. Fusion"},{"issue":"18","key":"4131_CR51","doi-asserted-by":"crossref","first-page":"7870","DOI":"10.3390\/s23187870","volume":"23","author":"H Li","year":"2023","unstructured":"Li, H., Xiao, Y., Cheng, C., Song, X.: SFPFusion: an improved vision transformer combining super feature attention and wavelet-guided pooling for infrared and visible images fusion. Sensors 23(18), 7870 (2023)","journal-title":"Sensors"},{"key":"4131_CR52","doi-asserted-by":"crossref","first-page":"101870","DOI":"10.1016\/j.inffus.2023.101870","volume":"99","author":"L Tang","year":"2023","unstructured":"Tang, L., Zhang, H., Xu, H., Ma, J.: Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity. Inf. Fusion 99, 101870 (2023)","journal-title":"Inf. Fusion"},{"key":"4131_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3280496","volume":"72","author":"L Ming","year":"2023","unstructured":"Ming, L., Jiang, M., Kong, J., Tao, X.: LDRepFM: a real-time end-to-end visible and infrared image fusion model based on layer decomposition and re-parameterization. IEEE Trans. Instrum. Meas. 72, 1\u201312 (2023). https:\/\/doi.org\/10.1109\/TIM.2023.3280496","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4131_CR54","doi-asserted-by":"crossref","first-page":"107804","DOI":"10.1016\/j.optlaseng.2023.107804","volume":"171","author":"M Xing","year":"2023","unstructured":"Xing, M., Liu, G., Tang, H., Qian, Y., Zhang, J.: Multi-level adaptive perception guidance based infrared and visible image fusion. Opt. Lasers Eng. 171, 107804 (2023)","journal-title":"Opt. Lasers Eng."},{"key":"4131_CR55","doi-asserted-by":"crossref","first-page":"107898","DOI":"10.1016\/j.engappai.2024.107898","volume":"132","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Pu, J., Miao, D., Zhang, L., Zhang, L., Du, X.: SCGRFuse: An infrared and visible image fusion network based on spatial\/channel attention mechanism and gradient aggregation residual dense blocks. Eng. Appl. Artif. Intell. 132, 107898 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4131_CR56","unstructured":"Song, Y., Cao, Z., Xiang, W., Long, S., Yang, B., Ge, H., Wu, C.: Troublemaker learning for low-light image enhancement. arXiv preprint arXiv:2402.04584, (2024)"},{"key":"4131_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2024.3390194","volume":"73","author":"J Chen","year":"2024","unstructured":"Chen, J., Yang, L., Liu, W., Tian, X., Ma, J.: LENFusion: a joint low-light enhancement and fusion network for nighttime infrared and visible image Fusion. IEEE Trans. Instrum. Meas. 73, 1\u201315 (2024). https:\/\/doi.org\/10.1109\/TIM.2024.3390194","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4131_CR58","doi-asserted-by":"crossref","first-page":"107905","DOI":"10.1016\/j.engappai.2024.107905","volume":"132","author":"Y Zhou","year":"2024","unstructured":"Zhou, Y., He, K., Xu, D., Tao, D., Lin, X., Li, C.: ASFusion: adaptive visual enhancement and structural patch decomposition for infrared and visible image fusion. Eng. Appl. Artif. Intell. 132, 107905 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4131_CR59","doi-asserted-by":"crossref","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. Inf. Fusion 91, 477\u2013493 (2023)","journal-title":"Inf. Fusion"},{"issue":"8","key":"4131_CR60","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"key":"4131_CR61","doi-asserted-by":"crossref","first-page":"109270","DOI":"10.1016\/j.patcog.2022.109270","volume":"137","author":"D Cheng","year":"2023","unstructured":"Cheng, D., Wang, G., Wang, B., Zhang, Q., Han, J., Zhang, D.: Hybrid routing transformer for zero-shot learning. Pattern Recogn. 137, 109270 (2023)","journal-title":"Pattern Recogn."},{"issue":"7","key":"4131_CR62","first-page":"3688","volume":"44","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Zhang, D., Zhang, Q., Han, J.: Part-object relational visual saliency. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3688\u20133704 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4131_CR63","doi-asserted-by":"crossref","first-page":"126916","DOI":"10.1016\/j.neucom.2023.126916","volume":"563","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Dong, X., Zhang, D., Xu, S.: Deep unsupervised part-whole relational visual saliency. Neurocomputing 563, 126916 (2024)","journal-title":"Neurocomputing"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04131-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04131-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04131-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T06:40:23Z","timestamp":1747636823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04131-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,3]]},"references-count":63,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["4131"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04131-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4494766\/v1","asserted-by":"object"}]},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,3]]},"assertion":[{"value":"29 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2025","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"511"}}