{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T04:44:27Z","timestamp":1777351467818,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Jilin Provincial Development and Reform Commission\u2019s special project for innovation ability construction"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The goal of infrared and visible image fusion in the night scene is to generate a fused image containing salient targets and rich textural details. However, the existing image fusion methods fail to take the unevenness of nighttime luminance into account. To address the above issue, an infrared and visible image fusion method for highlighting salient targets in the night scene is proposed. First of all, a global attention module is designed, which rescales the weights of different channels after capturing global contextual information. Second, the loss function is divided into the foreground loss and the background loss, forcing the fused image to retain rich texture details while highlighting the salient targets. Finally, a luminance estimation function is introduced to obtain the trade-off control parameters of the foreground loss function based on the nighttime luminance. It can effectively highlight salient targets by retaining the foreground information from the source images. Compared with other advanced methods, the experimental results adequately demonstrate the excellent fusion performance and generalization of the proposed method.<\/jats:p>","DOI":"10.3390\/e24121759","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T01:54:31Z","timestamp":1669859671000},"page":"1759","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Infrared and Visible Image Fusion for Highlighting Salient Targets in the Night Scene"],"prefix":"10.3390","volume":"24","author":[{"given":"Weida","family":"Zhan","sequence":"first","affiliation":[{"name":"National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiale","family":"Wang","sequence":"additional","affiliation":[{"name":"National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0178-9416","authenticated-orcid":false,"given":"Yichun","family":"Jiang","sequence":"additional","affiliation":[{"name":"National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6670-9707","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingyuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Hong","sequence":"additional","affiliation":[{"name":"National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"130778","DOI":"10.1109\/ACCESS.2021.3111905","article-title":"MIFFuse: A multi-level feature fusion network for infrared and visible images","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhu, D., Zhan, W., and Fu, J. (2022). RI-MFM: A Novel Infrared and Visible Image Registration with Rotation Invariance and Multilevel Feature Matching. Electronics, 11.","DOI":"10.3390\/electronics11182866"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.infrared.2019.03.022","article-title":"A moving target extraction algorithm based on the fusion of infrared and visible images","volume":"98","author":"Qiu","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TFUZZ.2022.3159099","article-title":"Generalized Point Set Registration with Fuzzy Correspondences Based on Variational Bayesian Inference","volume":"30","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, Z., and Zeng, S. (2022). TPFusion: Texture preserving fusion of infrared and visible images via dense networks. Entropy, 24.","DOI":"10.3390\/e24020294"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8808","DOI":"10.1109\/JSEN.2022.3161733","article-title":"IPLF: A Novel Image Pair Learning Fusion Network for Infrared and Visible Image","volume":"22","author":"Zhu","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","article-title":"IFCNN: A general image fusion framework based on convolutional neural network","volume":"54","author":"Zhang","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_9","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_10","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_11","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_12","doi-asserted-by":"crossref","unstructured":"Xu, D., Wang, Y., and Xu, S. (2020). Infrared and visible image fusion with a generative adversarial network and a residual network. Appl. Sci., 10.","DOI":"10.3390\/app10020554"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"206445","DOI":"10.1109\/ACCESS.2020.3037770","article-title":"Infrared and visible image fusion using a deep unsupervised framework with perceptual loss","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","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_15","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_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","unstructured":"Hou, J., Zhang, D., and Wu, W. (2021). A generative adversarial network for infrared and visible image fusion based on semantic segmentation. Entropy, 23.","DOI":"10.3390\/e23030376"},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, L., Yao, W., and Chen, C. (2022). Driving behavior recognition algorithm combining attention mechanism and lightweight network. Entropy, 24.","DOI":"10.3390\/e24070984"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hui, Y., Wang, J., and Shi, Y. (2022). Low Light Image Enhancement Algorithm Based on Detail Prediction and Attention Mechanism. Entropy, 24.","DOI":"10.3390\/e24060815"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/LGRS.2020.2988294","article-title":"SCAttNet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images","volume":"18","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tao, H., Geng, L., and Shan, S. (2022). Multi-Stream Convolution-Recurrent Neural Networks Based on Attention Mechanism Fusion for Speech Emotion Recognition. Entropy, 24.","DOI":"10.3390\/e24081025"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"4753","DOI":"10.1007\/s40747-022-00722-9","article-title":"MSAt-GAN: A generative adversarial network based on multi-scale and deep attention mechanism for infrared and visible light image fusion","volume":"8","author":"Li","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., and Gupta, A. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mishra, S., Liang, P., and Czajka, A. (2019, January 8\u201311). CC-NET: Image complexity guided network compression for biomedical image segmentation. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759448"},{"key":"ref_30","unstructured":"Zhu, Z., Xu, M., and Bai, S. (November, January 27). Asymmetric non-local neural networks for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Piao, J., Chen, Y., and Shin, H. (2019). A new deep learning based multi-spectral image fusion method. Entropy, 21.","DOI":"10.3390\/e21060570"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., and Doll\u00e1r, P. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","first-page":"023522","DOI":"10.1117\/1.2945910","article-title":"Assessment of image fusion procedures using entropy, image quality, and multispectral classification","volume":"2","author":"Roberts","year":"2008","journal-title":"J. Appl. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1049\/el:20020212","article-title":"Information measure for performance of image fusion","volume":"38","author":"Qu","year":"2002","journal-title":"Electron. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1088\/0957-0233\/8\/4\/002","article-title":"In-fibre Bragg grating sensors","volume":"8","author":"Rao","year":"1997","journal-title":"Meas. Sci. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1109\/26.477498","article-title":"Image quality measures and their performance","volume":"43","author":"Eskicioglu","year":"1995","journal-title":"IEEE Trans. Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.inffus.2011.08.002","article-title":"A new image fusion performance metric based on visual information fidelity","volume":"14","author":"Han","year":"2013","journal-title":"Inf. Fusion"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1049\/el:20000267","article-title":"Objective image fusion performance measure","volume":"36","author":"Xydeas","year":"2000","journal-title":"Electron. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., and Zhu, P. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/12\/1759\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:30:46Z","timestamp":1760146246000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/12\/1759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,30]]},"references-count":42,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["e24121759"],"URL":"https:\/\/doi.org\/10.3390\/e24121759","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,30]]}}}