{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:16:33Z","timestamp":1783577793758,"version":"3.55.0"},"reference-count":54,"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":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114400","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:10:42Z","timestamp":1783437042000},"page":"114400","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PD","title":["FreedomDiVe: Task-free image fusion via marginal distribution-based diffusion variational estimation"],"prefix":"10.1016","volume":"180","author":[{"given":"Zihan","family":"Gui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3410-0643","authenticated-orcid":false,"given":"Wei","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.114400_b1","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","article-title":"Image fusion meets deep learning: A survey and perspective","volume":"76","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114400_b2","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.inffus.2020.06.013","article-title":"Multi-focus image fusion: A survey of the state of the art","volume":"64","author":"Liu","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114400_b3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.inffus.2021.02.005","article-title":"Benchmarking and comparing multi-exposure image fusion algorithms","volume":"74","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114400_b4","first-page":"1","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":"10.1016\/j.patcog.2026.114400_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.113022","article-title":"Infrared and visible image fusion via iterative feature decomposition and deep balanced fusion","volume":"174","author":"Li","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114400_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2026.113130","article-title":"SETFusion: A semantic transformer for infrared and visible image fusion","volume":"175","author":"Tang","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114400_b7","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.inffus.2020.08.022","article-title":"MFF-GAN: An unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion","volume":"66","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114400_b8","doi-asserted-by":"crossref","first-page":"8668","DOI":"10.1109\/TIP.2020.3018261","article-title":"An \u03b1-matte boundary defocus model-based cascaded network for multi-focus image fusion","volume":"29","author":"Ma","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.114400_b9","doi-asserted-by":"crossref","first-page":"7203","DOI":"10.1109\/TIP.2020.2999855","article-title":"MEF-GAN: Multi-exposure image fusion via generative adversarial networks","volume":"29","author":"Xu","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.114400_b10","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.inffus.2021.10.006","article-title":"Multi-exposure image fusion via deep perceptual enhancement","volume":"79","author":"Han","year":"2022","journal-title":"Inf. Fusion"},{"issue":"12","key":"10.1016\/j.patcog.2026.114400_b11","doi-asserted-by":"crossref","first-page":"12624","DOI":"10.1109\/TCSVT.2024.3433555","article-title":"Co-enhancement of multi-modality image fusion and object detection via feature adaptation","volume":"34","author":"Dong","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.114400_b12","first-page":"1","article-title":"Sigfusion: semantic information-guided infrared and visible image fusion","volume":"73","author":"Lv","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"7","key":"10.1016\/j.patcog.2026.114400_b13","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."},{"issue":"1","key":"10.1016\/j.patcog.2026.114400_b14","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."},{"issue":"1","key":"10.1016\/j.patcog.2026.114400_b15","article-title":"Generative adversarial network: An overview of theory and applications","volume":"1","author":"Aggarwal","year":"2021","journal-title":"Int. J. Inf. Manag. Data Insights"},{"issue":"11","key":"10.1016\/j.patcog.2026.114400_b16","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"issue":"3","key":"10.1016\/j.patcog.2026.114400_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3446374","article-title":"Generative adversarial networks (GANs) challenges, solutions, and future directions","volume":"54","author":"Saxena","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.patcog.2026.114400_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.infrared.2025.105834","article-title":"Denoising diffusion based infrared and visible image fusion with transformer","volume":"148","author":"Chen","year":"2025","journal-title":"Infrared Phys. Technol."},{"key":"10.1016\/j.patcog.2026.114400_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102450","article-title":"Diff-IF: Multi-modality image fusion via diffusion model with fusion knowledge prior","volume":"110","author":"Yi","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114400_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2026.113174","article-title":"CUDiff: Consistency and uncertainty guided conditional diffusion for infrared and visible image fusion","volume":"176","author":"Luo","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114400_b21","article-title":"VDMUFusion: A versatile diffusion model-based unsupervised framework for image fusion","author":"Shi","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.114400_b22","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":"10.1016\/j.patcog.2026.114400_b23","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.inffus.2019.02.003","article-title":"Ensemble of CNN for multi-focus image fusion","volume":"51","author":"Amin-Naji","year":"2019","journal-title":"Inf. Fusion"},{"issue":"2","key":"10.1016\/j.patcog.2026.114400_b24","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/TCI.2018.2889959","article-title":"Multilevel features convolutional neural network for multifocus image fusion","volume":"5","author":"Yang","year":"2019","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"10.1016\/j.patcog.2026.114400_b25","doi-asserted-by":"crossref","unstructured":"K. Ram Prabhakar, V. Sai Srikar, R. Venkatesh Babu, Deepfuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 4714\u20134722.","DOI":"10.1109\/ICCV.2017.505"},{"issue":"5","key":"10.1016\/j.patcog.2026.114400_b26","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."},{"issue":"12","key":"10.1016\/j.patcog.2026.114400_b27","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":"10.1016\/j.patcog.2026.114400_b28","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114400_b29","series-title":"Denoising diffusion implicit models","author":"Song","year":"2020"},{"key":"10.1016\/j.patcog.2026.114400_b30","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":"10.1016\/j.patcog.2026.114400_b31","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.inffus.2022.07.013","article-title":"UIFGAN: An unsupervised continual-learning generative adversarial network for unified image fusion","volume":"88","author":"Le","year":"2022","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114400_b32","doi-asserted-by":"crossref","first-page":"26565","DOI":"10.52202\/068431-1926","article-title":"Elucidating the design space of diffusion-based generative models","volume":"35","author":"Karras","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"9","key":"10.1016\/j.patcog.2026.114400_b33","doi-asserted-by":"crossref","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion models in vision: A survey","volume":"45","author":"Croitoru","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114400_b34","doi-asserted-by":"crossref","unstructured":"Zixiang Zhao, Haowen Bai, Yuanzhi Zhu, Jiangshe Zhang, Shuang Xu, Yulun Zhang, Kai Zhang, Deyu Meng, Radu Timofte, Luc Van Gool, DDFM: denoising diffusion model for multi-modality image fusion, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 8082\u20138093.","DOI":"10.1109\/ICCV51070.2023.00742"},{"key":"10.1016\/j.patcog.2026.114400_b35","article-title":"FusionDiff: Multi-focus image fusion using denoising diffusion probabilistic models","volume":"238","author":"Li","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2026.114400_b36","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 Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.114400_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102639","article-title":"LFDT-fusion: A latent feature-guided diffusion transformer model for general image fusion","volume":"113","author":"Yang","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114400_b38","article-title":"TOFusion: Text-guided and object-aware infrared and visible image fusion","volume":"179","author":"Chen","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114400_b39","series-title":"Stochastic Differential Equations: An Introduction with Applications","first-page":"38","article-title":"Stochastic differential equations","author":"\u00d8ksendal","year":"2003"},{"key":"10.1016\/j.patcog.2026.114400_b40","unstructured":"Jinyuan Liu, Xin Fan, Zhanbo Huang, Guanyao Wu, Risheng Liu, Wei Zhong, Zhongxuan Luo, Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5802\u20135811."},{"key":"10.1016\/j.patcog.2026.114400_b41","doi-asserted-by":"crossref","unstructured":"Han Xu, Jiayi Ma, Zhuliang Le, Junjun Jiang, Xiaojie Guo, FusionDN: A Unified Densely Connected Network for Image Fusion, in: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020.","DOI":"10.1609\/aaai.v34i07.6936"},{"key":"10.1016\/j.patcog.2026.114400_b42","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.inffus.2014.10.004","article-title":"Multi-focus image fusion using dictionary-based sparse representation","volume":"25","author":"Nejati","year":"2015","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114400_b43","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.inffus.2020.08.022","article-title":"MFF-GAN: An unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion","volume":"66","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114400_b44","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.inffus.2021.02.005","article-title":"Benchmarking and comparing multi-exposure image fusion algorithms","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"issue":"4","key":"10.1016\/j.patcog.2026.114400_b45","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1109\/TIP.2018.2794218","article-title":"Learning a deep single image contrast enhancer from multi-exposure images","volume":"27","author":"Cai","year":"2018","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.patcog.2026.114400_b46","first-page":"1275","article-title":"Blind image quality assessment via deep learning","volume":"26","author":"Hou","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2026.114400_b47","doi-asserted-by":"crossref","unstructured":"Zixuan Chen, Yujin Wang, Xin Cai, Zhiyuan You, Zheming Lu, Fan Zhang, Shi Guo, Tianfan Xue, UltraFusion: Ultra high dynamic imaging using exposure fusion, in: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 16111\u201316121.","DOI":"10.1109\/CVPR52734.2025.01502"},{"key":"10.1016\/j.patcog.2026.114400_b48","series-title":"Image quality assessment using contrastive learning","author":"Madhusudana","year":"2021"},{"key":"10.1016\/j.patcog.2026.114400_b49","doi-asserted-by":"crossref","unstructured":"Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini, Alberto Del Bimbo, ARNIQA: Learning Distortion Manifold for Image Quality Assessment, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2024, pp. 189\u2013198.","DOI":"10.1109\/WACV57701.2024.00026"},{"key":"10.1016\/j.patcog.2026.114400_b50","doi-asserted-by":"crossref","unstructured":"Guanyao Wu, Haoyu Liu, Hongming Fu, Yichuan Peng, Jinyuan Liu, Xin Fan, Risheng Liu, Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond, in: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 17882\u201317891.","DOI":"10.1109\/CVPR52734.2025.01666"},{"key":"10.1016\/j.patcog.2026.114400_b51","doi-asserted-by":"crossref","unstructured":"Chunyang Cheng, Tianyang Xu, Zhenhua Feng, Xiaojun Wu, Zhangyong Tang, Hui Li, Zeyang Zhang, Sara Atito, Muhammad Awais, Josef Kittler, One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion, in: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 28102\u201328112.","DOI":"10.1109\/CVPR52734.2025.02617"},{"key":"10.1016\/j.patcog.2026.114400_b52","article-title":"Mask-difuser: A masked diffusion model for unified unsupervised image fusion","author":"Tang","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114400_b53","article-title":"Omnifuse: Composite degradation-robust image fusion with language-driven semantics","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114400_b54","doi-asserted-by":"crossref","unstructured":"Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326013658?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326013658?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:24:24Z","timestamp":1783574664000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326013658"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":54,"alternative-id":["S0031320326013658"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114400","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FreedomDiVe: Task-free image fusion via marginal distribution-based diffusion variational estimation","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114400","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":"114400"}}