{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T14:14:16Z","timestamp":1788185656900,"version":"build-2803163510"},"reference-count":42,"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\/501100018543","name":"Natural Science Foundation of Tibet Autonomous Region","doi-asserted-by":"publisher","award":["2025D01C55"],"award-info":[{"award-number":["2025D01C55"]}],"id":[{"id":"10.13039\/501100018543","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.2026.114501","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T23:27:37Z","timestamp":1784244457000},"page":"114501","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PE","title":["A Retinex-based frequency-gradient optimization framework for infrared and visible image fusion"],"prefix":"10.1016","volume":"180","author":[{"given":"Yuanwei","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zilong","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyoukang","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaozheng","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1625-686X","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.patcog.2026.114501_b1","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 Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114501_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111177","article-title":"An effective multi-scale interactive fusion network with hybrid transformer and CNN for smoke image segmentation","volume":"159","author":"Li","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114501_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111476","article-title":"Infrared and visible image fusion via dual encoder based on dense connection","volume":"163","author":"Lu","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114501_b4","series-title":"International Conference on Biologically Inspired Techniques in Many-Criteria Decision-Making Technologies","first-page":"346","article-title":"A deep learning approach to image fusion: Combining visual and infrared images with DCGAN","author":"Jaiswal","year":"2024"},{"issue":"1","key":"10.1016\/j.patcog.2026.114501_b5","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":"3","key":"10.1016\/j.patcog.2026.114501_b6","first-page":"358","article-title":"DSEL-CNN: Image fusion algorithm combining attention mechanism and balanced loss","volume":"47","author":"Yating","year":"2025","journal-title":"Infrared Technol."},{"key":"10.1016\/j.patcog.2026.114501_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114313","article-title":"CTIUFuse: A CNN-transformer-based iterative feature universal fusion algorithm for multimodal images","author":"Lin","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.patcog.2026.114501_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111041","article-title":"MMAE: A universal image fusion method via mask attention mechanism","volume":"158","author":"Wang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114501_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111594","article-title":"HaarFuse: A dual-branch infrared and visible light image fusion network based on haar wavelet transform","volume":"164","author":"Wang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114501_b10","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.2026.114501_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.image.2021.116295","article-title":"MFIF-GAN: A new generative adversarial network for multi-focus image fusion","volume":"96","author":"Wang","year":"2021","journal-title":"Signal Process., Image Commun."},{"issue":"5","key":"10.1016\/j.patcog.2026.114501_b12","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1007\/s11263-024-02256-8","article-title":"Refusion: Learning image fusion from reconstruction with learnable loss via meta-learning","volume":"133","author":"Bai","year":"2025","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.patcog.2026.114501_b13","doi-asserted-by":"crossref","unstructured":"Z. Zhao, H. Bai, Y. Zhu, J. Zhang, S. Xu, Y. Zhang, K. Zhang, D. Meng, R. Timofte, L. Van Gool, DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2023, pp. 8082\u20138093.","DOI":"10.1109\/ICCV51070.2023.00742"},{"key":"10.1016\/j.patcog.2026.114501_b14","article-title":"FusionINV: A diffusion-based approach for multimodal image fusion","author":"Liang","year":"2025","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.114501_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.109080","article-title":"SNM: A medical image fusion method using SSFRG decomposition, NRBOFA base fusion, and multi-level morphological gradient","volume":"113","author":"Nguyen","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.patcog.2026.114501_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105315","article-title":"Enhancing medical image fusion through advanced decomposition and optimization methods","volume":"165","author":"Dinh","year":"2025","journal-title":"Digit. Signal Process."},{"issue":"16","key":"10.1016\/j.patcog.2026.114501_b17","doi-asserted-by":"crossref","first-page":"10037","DOI":"10.1007\/s00521-025-11072-1","article-title":"An effective medical image fusion method utilizing moth-flame optimization and coupled neural P systems","volume":"37","author":"Dinh","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.patcog.2026.114501_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106976","article-title":"MIF-BTF-MRN: medical image fusion based on the bilateral texture filter and transfer learning with the ResNet-101 network","volume":"100","author":"Dinh","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"issue":"8","key":"10.1016\/j.patcog.2026.114501_b19","doi-asserted-by":"crossref","first-page":"4325","DOI":"10.1007\/s00521-023-09294-2","article-title":"Medical image fusion based on transfer learning techniques and coupled neural P systems","volume":"36","author":"Dinh","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.patcog.2026.114501_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2026.106109","article-title":"HDAFusion: Hierarchical decomposition and attention-based framework for infrared and visible image fusion","author":"Dinh","year":"2026","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.patcog.2026.114501_b21","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":"10.1016\/j.patcog.2026.114501_b22","doi-asserted-by":"crossref","unstructured":"H. Bai, J. Zhang, Z. Zhao, L. Deng, Y. Cui, S. Xu, Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2025.","DOI":"10.1109\/ICCV51701.2025.00681"},{"key":"10.1016\/j.patcog.2026.114501_b23","article-title":"A retinex decomposition model-based deep framework for infrared and visible image fusion","author":"Wang","year":"2024","journal-title":"IEEE J. Sel. Top. Signal Process."},{"issue":"16","key":"10.1016\/j.patcog.2026.114501_b24","doi-asserted-by":"crossref","first-page":"11733","DOI":"10.1007\/s00521-023-08314-5","article-title":"An interactive deep model combined with retinex for low-light visible and infrared image fusion","volume":"35","author":"Wang","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.patcog.2026.114501_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.image.2023.116956","article-title":"Retinex-MPCNN: a retinex and modified pulse coupled neural network based method for low-illumination visible and infrared image fusion","volume":"115","author":"Zhou","year":"2023","journal-title":"Signal Process., Image Commun."},{"key":"10.1016\/j.patcog.2026.114501_b26","doi-asserted-by":"crossref","unstructured":"P. Das, M. Gevers, S. Karaoglu, T. Gevers, Idtransformer: Transformer for intrinsic image decomposition, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 816\u2013825.","DOI":"10.1109\/ICCVW60793.2023.00089"},{"key":"10.1016\/j.patcog.2026.114501_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.aml.2023.108782","article-title":"A weighted gradient model with total variation regularization for multi-focus image fusion","volume":"145","author":"Wen","year":"2023","journal-title":"Appl. Math. Lett."},{"issue":"11","key":"10.1016\/j.patcog.2026.114501_b28","doi-asserted-by":"crossref","first-page":"7332","DOI":"10.1109\/TCSVT.2022.3181781","article-title":"Self-supervised low-light image enhancement using discrepant untrained network priors","volume":"32","author":"Liang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"10.1016\/j.patcog.2026.114501_b29","doi-asserted-by":"crossref","first-page":"14280","DOI":"10.1038\/s41598-025-88261-4","article-title":"Multi-scene image fusion via memory aware synapses","volume":"15","author":"Meng","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.patcog.2026.114501_b30","series-title":"Yolov11: An overview of the key architectural enhancements","author":"Khanam","year":"2024"},{"issue":"7","key":"10.1016\/j.patcog.2026.114501_b31","doi-asserted-by":"crossref","first-page":"3159","DOI":"10.1109\/TCSVT.2023.3234340","article-title":"DATFuse: Infrared and visible image fusion via dual attention transformer","volume":"33","author":"Tang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.114501_b32","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"},{"issue":"10","key":"10.1016\/j.patcog.2026.114501_b33","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":"10.1016\/j.patcog.2026.114501_b34","doi-asserted-by":"crossref","unstructured":"Z. Wang, J. Zhang, H. Song, M. Ge, J. Wang, H. Duan, Highlight What You Want: Weakly-Supervised Instance-Level Controllable Infrared-Visible Image Fusion, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2025, pp. 12637\u201312647.","DOI":"10.1109\/ICCV51701.2025.01174"},{"issue":"7","key":"10.1016\/j.patcog.2026.114501_b35","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":"10.1016\/j.patcog.2026.114501_b36","first-page":"1","article-title":"Lenfusion: a joint low-light enhancement and fusion network for nighttime infrared and visible image fusion","volume":"73","author":"Chen","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.patcog.2026.114501_b37","article-title":"A degradation-aware guided fusion network for infrared and visible image","author":"Wang","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114501_b38","doi-asserted-by":"crossref","unstructured":"C. Guo, C. Li, J. Guo, C.C. Loy, J. Hou, S. Kwong, R. Cong, Zero-reference deep curve estimation for low-light image enhancement, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 1780\u20131789.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"10.1016\/j.patcog.2026.114501_b39","series-title":"European Conference on Computer Vision","first-page":"573","article-title":"Lednet: Joint low-light enhancement and deblurring in the dark","author":"Zhou","year":"2022"},{"key":"10.1016\/j.patcog.2026.114501_b40","doi-asserted-by":"crossref","unstructured":"Y. Zhang, J. Zhang, X. Guo, Kindling the darkness: A practical low-light image enhancer, in: Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 1632\u20131640.","DOI":"10.1145\/3343031.3350926"},{"key":"10.1016\/j.patcog.2026.114501_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2023.104020","article-title":"A review of image fusion: Methods, applications and performance metrics","volume":"137","author":"Singh","year":"2023","journal-title":"Digit. Signal Process."},{"issue":"1","key":"10.1016\/j.patcog.2026.114501_b42","doi-asserted-by":"crossref","first-page":"16838","DOI":"10.1038\/s41598-023-44179-3","article-title":"Underwater image quality assessment method based on color space multi-feature fusion","volume":"13","author":"Chen","year":"2023","journal-title":"Sci. Rep."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326014652?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326014652?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:20:12Z","timestamp":1788182412000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326014652"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":42,"alternative-id":["S0031320326014652"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114501","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":"A Retinex-based frequency-gradient optimization framework for infrared and visible image fusion","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114501","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"114501"}}