{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:25:13Z","timestamp":1776277513154,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T00:00:00Z","timestamp":1724544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072328"],"award-info":[{"award-number":["62072328"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The main purpose of infrared and visible image fusion is to produce a fusion image that incorporates less redundant information while incorporating more complementary information, thereby facilitating subsequent high-level visual tasks. However, obtaining complementary information from different modalities of images is a challenge. Existing fusion methods often consider only relevance and neglect the complementarity of different modalities\u2019 features, leading to the loss of some cross-modal complementary information. To enhance complementary information, it is believed that more comprehensive cross-modal interactions should be provided. Therefore, a fusion network for infrared and visible fusion is proposed, which is based on bilateral cross-feature interaction, termed BCMFIFuse. To obtain features in images of different modalities, we devise a two-stream network. During the feature extraction, a cross-modal feature correction block (CMFC) is introduced, which calibrates the current modality features by leveraging feature correlations from different modalities in both spatial and channel dimensions. Then, a feature fusion block (FFB) is employed to effectively integrate cross-modal information. The FFB aims to explore and integrate the most discriminative features from the infrared and visible image, enabling long-range contextual interactions to enhance global cross-modal features. In addition, to extract more comprehensive multi-scale features, we develop a hybrid pyramid dilated convolution block (HPDCB). Comprehensive experiments on different datasets reveal that our method performs excellently in qualitative, quantitative, and object detection evaluations.<\/jats:p>","DOI":"10.3390\/rs16173136","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T03:14:31Z","timestamp":1724642071000},"page":"3136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["BCMFIFuse: A Bilateral Cross-Modal Feature Interaction-Based Network for Infrared and Visible Image Fusion"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0284-5229","authenticated-orcid":false,"given":"Xueyan","family":"Gao","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300350, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2353-5318","authenticated-orcid":false,"given":"Shiguang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300350, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101828","DOI":"10.1016\/j.inffus.2023.101828","article-title":"An interactively reinforced paradigm for joint infrared-visible image fusion and saliency object detection","volume":"98","author":"Wang","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.inffus.2022.12.007","article-title":"AT-GAN: A generative adversarial network with attention and transition for infrared and visible image fusion","volume":"92","author":"Rao","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wei, Q., Liu, Y., Jiang, X., Zhang, B., Su, Q., and Yu, M. (2024). DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion. Remote Sens., 16.","DOI":"10.3390\/rs16101795"},{"key":"ref_4","first-page":"5218814","article-title":"A Dual-Domain Super-Resolution Image Fusion Method with SIRV and GALCA Model for PolSAR and Panchromatic Images","volume":"60","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, J., Fan, X., Huang, Z., Wu, G., Liu, R., Zhong, W., and Luo, Z. (2022, January 18\u201324). Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13023","DOI":"10.1109\/TITS.2022.3232153","article-title":"Edge intelligence empowered vehicle detection and image segmentation for autonomous vehicles","volume":"24","author":"Chen","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","first-page":"5004213","article-title":"RI-Fusion: 3D object detection using enhanced point features with range-image fusion for autonomous driving","volume":"72","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","article-title":"FusionGAN: A generative adversarial network for infrared and visible image fusion","volume":"48","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110633","DOI":"10.1016\/j.patcog.2024.110633","article-title":"DSFusion: Infrared and visible image fusion method combining detail and scene information","volume":"154","author":"Liu","year":"2024","journal-title":"Pattern Recogn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2121","DOI":"10.1109\/JAS.2022.106082","article-title":"SuperFusion: A versatile image registration and fusion network with semantic awareness","volume":"9","author":"Tang","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.infrared.2017.04.018","article-title":"Infrared and visible image fusion method based on saliency detection in sparse domain","volume":"83","author":"Liu","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_12","first-page":"5001805","article-title":"Hyperspectral and multispectral image fusion via variational tensor subspace decomposition","volume":"19","author":"Xing","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.inffus.2017.05.006","article-title":"Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review","volume":"40","author":"Zhang","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"16040","DOI":"10.1109\/ACCESS.2017.2735865","article-title":"From multi-scale decomposition to non-multi-scale decomposition methods: A comprehensive survey of image fusion techniques and its applications","volume":"5","author":"Dogra","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"59976","DOI":"10.1109\/ACCESS.2020.2982712","article-title":"Improving the performance of image fusion based on visual saliency weight map combined with CNN","volume":"8","author":"Yan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1109\/TIP.2020.2977573","article-title":"DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion","volume":"29","author":"Ma","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"127391","DOI":"10.1016\/j.neucom.2024.127391","article-title":"DUGAN: Infrared and visible image fusion based on dual fusion paths and a U-type discriminator","volume":"578","author":"Chang","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","article-title":"DenseFuse: A fusion approach to infrared and visible images","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Xu, S., Zhang, C., Liu, J., Li, P., and Zhang, J. (2021, January 7\u201315). DIDFuse: Deep image decomposition for infrared and visible image fusion. Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, Yokohama, Japan.","DOI":"10.24963\/ijcai.2020\/135"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, X., Gao, H., Miao, Q., Xi, Y., Ai, Y., and Gao, D. (2022). MFST: Multi-modal feature self-adaptive transformer for infrared and visible image fusion. Remote Sens., 14.","DOI":"10.3390\/rs14133233"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1109\/TIM.2020.3005230","article-title":"NestFuse: An infrared and visible image fusion architecture based on nest connection and spatial\/channel attention models","volume":"69","author":"Li","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","article-title":"Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network","volume":"82","author":"Tang","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.inffus.2022.10.034","article-title":"DIVFusion: Darkness-free infrared and visible image fusion","volume":"91","author":"Tang","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5009513","DOI":"10.1109\/TIM.2021.3075747","article-title":"STDFusionNet: An infrared and visible image fusion network based on salient target detection","volume":"70","author":"Ma","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","article-title":"PIAFusion: A progressive infrared and visible image fusion network based on illumination aware","volume":"83","author":"Tang","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5016412","DOI":"10.1109\/TIM.2022.3216413","article-title":"SwinFuse: A residual swin transformer fusion network for infrared and visible images","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","first-page":"12484","article-title":"FusionDN: A unified densely connected network for image fusion","volume":"34","author":"Xu","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_29","first-page":"5005014","article-title":"GANMcC: A generative adversarial network with multiclassification constraints for infrared and visible image fusion","volume":"70","author":"Ma","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s11263-021-01501-8","article-title":"SDNet: A versatile squeeze-and-decomposition network for real-time image fusion","volume":"129","author":"Zhang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","first-page":"12797","article-title":"Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity","volume":"34","author":"Zhang","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.inffus.2021.02.023","article-title":"RFN-Nest: An end-to-end residual fusion network for infrared and visible images","volume":"73","author":"Li","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1117\/1.1303728","article-title":"Contrast-based image fusion using the discrete wavelet transform","volume":"39","author":"Pu","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.inffus.2015.06.003","article-title":"Gradient entropy metric and p-laplace diffusion constraint-based algorithm for noisy multispectral image fusion","volume":"27","author":"Zhao","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_35","unstructured":"Zhao, Z., Xu, S., Zhang, C., Liu, J., and Zhang, J. (2020). Efficient and interpretable infrared and visible image fusion via algorithm unrolling. arXiv."},{"key":"ref_36","first-page":"5002215","article-title":"SEDRFuse: A symmetric encoder\u2013decoder with residual block network for infrared and visible image fusion","volume":"70","author":"Jian","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","first-page":"5005012","article-title":"Res2Fusion: Infrared and visible image fusion based on dense Res2net and double nonlocal attention models","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.inffus.2020.11.009","article-title":"RXDNFuse: A aggregated residual dense network for infrared and visible image fusion","volume":"69","author":"Long","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","article-title":"U2Fusion: A unified unsupervised image fusion network","volume":"44","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.inffus.2019.07.005","article-title":"Infrared and visible image fusion via detail preserving adversarial learning","volume":"54","author":"Ma","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1109\/TCI.2021.3119954","article-title":"GAN-FM: Infrared and visible image fusion using GAN with full-scale skip connection and dual Markovian discriminators","volume":"7","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/TMM.2020.2997127","article-title":"AttentionFGAN: Infrared and visible image fusion using attention-based generative adversarial networks","volume":"23","author":"Li","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4771","DOI":"10.1109\/TCSVT.2021.3054584","article-title":"Infrared and visible image fusion via texture conditional generative adversarial network","volume":"31","author":"Yang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5413","DOI":"10.1109\/TMM.2022.3192661","article-title":"YDTR: Infrared and Visible Image Fusion via Y-shape Dynamic Transformer","volume":"25","author":"Tang","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Gavaskar, R.G., and Chaudhury, K.N. (2019, January 20\u201323). Saliency guided image detail enhancement. Proceedings of the 2019 National Conference on Communications (NCC), Bangalore, India.","DOI":"10.1109\/NCC.2019.8732250"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rao, D., Xu, T., and Wu, X.-J. (2023). TGFuse: An infrared and visible image fusion approach based on transformer and generative adversarial network. IEEE Trans. Image Process., 1\u201312.","DOI":"10.1109\/TIP.2023.3273451"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jia, X., Zhu, C., Li, M., Tang, W., and Zhou, W. (2021, January 10\u201317). LLVIP: A visible-infrared paired dataset for low-light vision. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00389"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3562","DOI":"10.1049\/iet-ipr.2020.0360","article-title":"Infrared and visible image fusion using a shallow CNN and structural similarity constraint","volume":"14","author":"Li","year":"2020","journal-title":"IET Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.02.001","article-title":"Infrared and visible image fusion via gradient transfer and total variation minimization","volume":"31","author":"Ma","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, Z., Liu, J., Wu, G., Ma, L., Fan, X., and Liu, R. (2023). Bi-level dynamic learning for jointly multi-modality image fusion and beyond. arXiv.","DOI":"10.24963\/ijcai.2023\/138"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zheng, N., Zhou, M., Huang, J., Hou, J., Li, H., Xu, Y., and Zhao, F. (2024, January 17\u201321). Probing Synergistic High-Order Interaction in Infrared and Visible Image Fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02492"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhu, P., Sun, Y., Cao, B., and Hu, Q. (2024, January 17\u201321). Task-customized mixture of adapters for general image fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00678"},{"key":"ref_53","first-page":"1890","article-title":"A new image quality metric for image fusion: The sum of the correlations of differences","volume":"69","author":"Aslantas","year":"2015","journal-title":"AEU-Int. J. Electron. C"},{"key":"ref_54","first-page":"484","article-title":"Image fusion and image quality assessment of fused images","volume":"4","author":"Deshmukh","year":"2010","journal-title":"Int. J. Image Process. (IJIP)"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1109\/TCSVT.2018.2821177","article-title":"Multi-focus image fusion with a natural enhancement via a joint multi-level deeply supervised convolutional neural network","volume":"29","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ye, P., and Xiao, G. (2020, January 14\u201319). VIFB: A visible and infrared image fusion benchmark. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00060"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","article-title":"Infrared and visible image fusion methods and applications: A survey","volume":"45","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"55","DOI":"10.5815\/ijigsp.2014.10.07","article-title":"Edge detection operators: Peak signal to noise ratio based comparison","volume":"10","author":"Poobathy","year":"2014","journal-title":"Int. J. Image Graph. Signal Process."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Petrovic, V., and Xydeas, C. (2005, January 17\u201321). Objective image fusion performance characterisation. Proceedings of the Tenth IEEE International Conference on Computer Vision, Beijing, China.","DOI":"10.1109\/ICCV.2005.175"},{"key":"ref_60","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.compeleceng.2011.07.012","article-title":"A non-reference image fusion metric based on mutual information of image features","volume":"37","author":"Haghighat","year":"2011","journal-title":"Comput. Electr. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:42:35Z","timestamp":1760110955000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,25]]},"references-count":62,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173136"],"URL":"https:\/\/doi.org\/10.3390\/rs16173136","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,25]]}}}