{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:48:41Z","timestamp":1782492521928,"version":"3.54.5"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.neunet.2026.109276","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T15:53:26Z","timestamp":1782143606000},"page":"109276","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DMWFuse: A degradation-adaptive unified framework for RGB-IR image fusion"],"prefix":"10.1016","volume":"204","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3480-881X","authenticated-orcid":false,"given":"Keming","family":"Bai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linyuan","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiping","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2170-2369","authenticated-orcid":false,"given":"Jiahao","family":"Dang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacheng","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingzhao","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109276_bib0001","unstructured":"Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv: 2004.10934."},{"key":"10.1016\/j.neunet.2026.109276_bib0002","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.1109\/TIP.2025.3571339","article-title":"SDSFusion: A semantic-aware infrared and visible image fusion network for degraded scenes","volume":"34","author":"Chen","year":"2025","journal-title":"IEEE Transactions on Image Processing"},{"issue":"4","key":"10.1016\/j.neunet.2026.109276_bib0003","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109276_bib0004","series-title":"Proceedings of the European conference on computer vision (ECCV)","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.neunet.2026.109276_bib0005","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"3496","article-title":"LLVIP: A visible-infrared paired dataset for low-light vision","author":"Jia","year":"2021"},{"key":"10.1016\/j.neunet.2026.109276_bib0006","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TPAMI.2025.3559891","article-title":"MB-TaylorFormer V2: Improved multi-branch linear transformer expanded by Taylor formula for image restoration","volume":"47","author":"Jin","year":"2025","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"10.1016\/j.neunet.2026.109276_bib0007","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1007\/s11263-023-01948-x","article-title":"A deep learning framework for infrared and visible image fusion without strict registration","volume":"132","author":"Li","year":"2024","journal-title":"International Journal of Computer Vision"},{"issue":"5","key":"10.1016\/j.neunet.2026.109276_bib0008","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 Transactions on Image Processing"},{"key":"10.1016\/j.neunet.2026.109276_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102147","article-title":"CrossFuse: A novel cross attention mechanism based infrared and visible image fusion approach","volume":"103","author":"Li","year":"2024","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109276_bib0010","series-title":"2018 24th international conference on pattern recognition (ICPR)","first-page":"2705","article-title":"Infrared and visible image fusion using a deep learning framework","author":"Li","year":"2018"},{"issue":"9","key":"10.1016\/j.neunet.2026.109276_bib0011","doi-asserted-by":"crossref","first-page":"11040","DOI":"10.1109\/TPAMI.2023.3268209","article-title":"LRRNet: A novel representation learning guided fusion network for infrared and visible images","volume":"45","author":"Li","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109276_bib0012","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"21694","article-title":"MSeg3D: Multi-modal 3D semantic segmentation for autonomous driving","author":"Li","year":"2023"},{"key":"10.1016\/j.neunet.2026.109276_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2026.104130","article-title":"All-weather multi-modality image fusion: Unified framework and 100k benchmark","volume":"131","author":"Li","year":"2026","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109276_bib0014","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"7534","article-title":"DifIISR: A diffusion model with gradient guidance for infrared image super-resolution","author":"Li","year":"2025"},{"key":"10.1016\/j.neunet.2026.109276_bib0015","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"5802","article-title":"Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection","author":"Liu","year":"2022"},{"key":"10.1016\/j.neunet.2026.109276_bib0016","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"5802","article-title":"Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection","author":"Liu","year":"2022"},{"issue":"5","key":"10.1016\/j.neunet.2026.109276_bib0017","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1007\/s11263-023-01952-1","article-title":"CoCoNet: Coupled contrastive learning network with multi-level feature ensemble for multi-modality image fusion","volume":"132","author":"Liu","year":"2024","journal-title":"International Journal of Computer Vision"},{"key":"10.1016\/j.neunet.2026.109276_bib0018","doi-asserted-by":"crossref","unstructured":"Liu, Z., Liu, J., Wu, G., Ma, L., Fan, X., & Liu, R. (2023). Bi-level dynamic learning for jointly multi-modality image fusion and beyond. arXiv preprint arXiv: 2305.06720.","DOI":"10.24963\/ijcai.2023\/138"},{"issue":"7","key":"10.1016\/j.neunet.2026.109276_bib0019","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 Journal of Automatica Sinica"},{"key":"10.1016\/j.neunet.2026.109276_bib0020","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":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109276_bib0021","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/TIP.2024.3512365","article-title":"VDMUFusion: A versatile diffusion model-based unsupervised framework for image fusion","volume":"34","author":"Shi","year":"2024","journal-title":"IEEE Transactions on Image Processing"},{"issue":"10","key":"10.1016\/j.neunet.2026.109276_bib0022","doi-asserted-by":"crossref","first-page":"6700","DOI":"10.1109\/TCSVT.2022.3168279","article-title":"Drone-based RGB-infrared cross-modality vehicle detection via uncertainty-aware learning","volume":"32","author":"Sun","year":"2022","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.neunet.2026.109276_bib0023","series-title":"International conference on machine learning","first-page":"57571","article-title":"Task-gated multi-expert collaboration network for degraded multi-modal image fusion","author":"Sun","year":"2025"},{"key":"10.1016\/j.neunet.2026.109276_bib0024","series-title":"Proceedings of the 32nd ACM international conference on multimedia","first-page":"8546","article-title":"DRMF: Degradation-robust multi-modal image fusion via composable diffusion prior","author":"Tang","year":"2024"},{"key":"10.1016\/j.neunet.2026.109276_bib0025","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":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109276_bib0026","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":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109276_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109295","article-title":"TCCFusion: An infrared and visible image fusion method based on transformer and cross correlation","volume":"137","author":"Tang","year":"2023","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.109276_bib0028","series-title":"Proceedings of the IEEE\/CVF winter conference on applications of computer vision","first-page":"478","article-title":"Towards online domain adaptive object detection","author":"VS","year":"2023"},{"key":"10.1016\/j.neunet.2026.109276_bib0029","series-title":"2022 IEEE International conference on image processing (ICIP)","first-page":"3566","article-title":"Image fusion transformer","author":"Vs","year":"2022"},{"key":"10.1016\/j.neunet.2026.109276_bib0030","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"7464","article-title":"Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","author":"Wang","year":"2023"},{"issue":"10","key":"10.1016\/j.neunet.2026.109276_bib0031","doi-asserted-by":"crossref","first-page":"4541","DOI":"10.1007\/s11263-024-02056-0","article-title":"GridFormer: Residual dense transformer with grid structure for image restoration in adverse weather conditions","volume":"132","author":"Wang","year":"2024","journal-title":"International Journal Of Computer Vision"},{"key":"10.1016\/j.neunet.2026.109276_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112081","article-title":"All-in-one adverse weather removal via dual state space-based diffusion model with degradation-aware guidance","volume":"171","author":"Xie","year":"2026","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.109276_bib0033","series-title":"The thirty-ninth annual conference on neural information processing systems","article-title":"Deno-IF: Unsupervised noisy visible and infrared image fusion method","author":"Xu","year":"2025"},{"issue":"1","key":"10.1016\/j.neunet.2026.109276_bib0034","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 Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109276_bib0035","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1109\/TCI.2021.3100986","article-title":"Classification saliency-based rule for visible and infrared image fusion","volume":"7","author":"Xu","year":"2021","journal-title":"IEEE Transactions on Computational Imaging"},{"key":"10.1016\/j.neunet.2026.109276_bib0036","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"27026","article-title":"Text-if: Leveraging semantic text guidance for degradation-aware and interactive image fusion","author":"Yi","year":"2024"},{"key":"10.1016\/j.neunet.2026.109276_bib0037","doi-asserted-by":"crossref","first-page":"5705","DOI":"10.1109\/TIP.2023.3322046","article-title":"Dif-Fusion: Toward high color fidelity in infrared and visible image fusion with diffusion models","volume":"32","author":"Yue","year":"2023","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.neunet.2026.109276_bib0038","doi-asserted-by":"crossref","first-page":"39552","DOI":"10.52202\/079017-1250","article-title":"Text-difuse: An interactive multi-modal image fusion framework based on text-modulated diffusion model","volume":"37","author":"Zhang","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109276_bib0039","doi-asserted-by":"crossref","first-page":"7577","DOI":"10.1109\/TPAMI.2025.3568433","article-title":"OmniFuse: Composite degradation-robust image fusion with language-driven semantics","volume":"47","author":"Zhang","year":"2025","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109276_bib0040","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"26487","article-title":"Dispel darkness for better fusion: A controllable visual enhancer based on cross-modal conditional adversarial learning","author":"Zhang","year":"2024"},{"issue":"1","key":"10.1016\/j.neunet.2026.109276_bib0041","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1109\/TPAMI.2022.3148707","article-title":"Enhanced spatio-temporal interaction learning for video deraining: Faster and better","volume":"45","author":"Zhang","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109276_bib0042","doi-asserted-by":"crossref","first-page":"7419","DOI":"10.1109\/TIP.2021.3104166","article-title":"Deep dense multi-scale network for snow removal using semantic and depth priors","volume":"30","author":"Zhang","year":"2021","journal-title":"IEEE Transactions on Image Processing"},{"issue":"1","key":"10.1016\/j.neunet.2026.109276_bib0043","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TIP.2018.2867733","article-title":"Adversarial spatio-temporal learning for video deblurring","volume":"28","author":"Zhang","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"issue":"8","key":"10.1016\/j.neunet.2026.109276_bib0044","doi-asserted-by":"crossref","first-page":"10535","DOI":"10.1109\/TPAMI.2023.3261282","article-title":"Visible and infrared image fusion using deep learning","volume":"45","author":"Zhang","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109276_bib0045","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"5906","article-title":"CDDFuse: Correlation-driven dual-branch feature decomposition for multi-modality image fusion","author":"Zhao","year":"2023"},{"key":"10.1016\/j.neunet.2026.109276_bib0046","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"8082","article-title":"DDFM: Denoising diffusion model for multi-modality image fusion","author":"Zhao","year":"2023"},{"key":"10.1016\/j.neunet.2026.109276_bib0047","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1109\/TPAMI.2024.3475485","article-title":"Probing synergistic high-order interaction for multi-modal image fusion","volume":"47","author":"Zhou","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026007367?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026007367?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:11:47Z","timestamp":1782490307000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026007367"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":47,"alternative-id":["S0893608026007367"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109276","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DMWFuse: A degradation-adaptive unified framework for RGB-IR image fusion","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109276","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109276"}}