{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:59:01Z","timestamp":1781225941762,"version":"3.54.1"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"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","award":["51827813"],"award-info":[{"award-number":["51827813"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2022JBQY009"],"award-info":[{"award-number":["2022JBQY009"]}],"id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB2603302"],"award-info":[{"award-number":["2022YFB2603302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.115994","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:33:47Z","timestamp":1776144827000},"page":"115994","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Dual-model joint prompting for image dehazing"],"prefix":"10.1016","volume":"343","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0828-4023","authenticated-orcid":false,"given":"Yanting","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4226-4368","authenticated-orcid":false,"given":"Hui","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8367-9057","authenticated-orcid":false,"given":"Ying","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6690-1819","authenticated-orcid":false,"given":"Qianqian","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115994_b1","series-title":"IEEE Int. Conf. on Robot. and Autom.","first-page":"9722","article-title":"CNN-based simultaneous dehazing and depth estimation","author":"Lee","year":"2020"},{"key":"10.1016\/j.knosys.2026.115994_b2","doi-asserted-by":"crossref","unstructured":"Z. Zhang, L. Zhao, Y. Liu, S. Zhang, J. Yang, Unified Density-Aware Image Dehazing and Object Detection in Real-World Hazy Scenes, in: Proc. Asian Conf. on Comput. Vis., 2020.","DOI":"10.1007\/978-3-030-69538-5_8"},{"key":"10.1016\/j.knosys.2026.115994_b3","first-page":"1","article-title":"Detection-friendly dehazing: Object detection in real-world hazy scenes","author":"Li","year":"2023","journal-title":"PAMI"},{"key":"10.1016\/j.knosys.2026.115994_b4","doi-asserted-by":"crossref","unstructured":"X. Qin, Z. Wang, Y. Bai, X. Xie, H. Jia, FFA-Net: Feature Fusion Attention Network for Single Image Dehazing, in: Proc. AAAI Conf. Artif. Intell., 2020, pp. 11908\u201311915.","DOI":"10.1609\/aaai.v34i07.6865"},{"key":"10.1016\/j.knosys.2026.115994_b5","article-title":"EAA-net: A novel edge assisted attention network for single image dehazing","volume":"228","author":"Wang","year":"2021","journal-title":"KBS"},{"key":"10.1016\/j.knosys.2026.115994_b6","article-title":"Single image dehazing with an independent detail-recovery network","volume":"254","author":"Li","year":"2022","journal-title":"KBS"},{"key":"10.1016\/j.knosys.2026.115994_b7","article-title":"Haze transfer and feature aggregation network for real-world single image dehazing","volume":"251","author":"Li","year":"2022","journal-title":"KBS"},{"key":"10.1016\/j.knosys.2026.115994_b8","doi-asserted-by":"crossref","unstructured":"Y. Zheng, J. Zhan, S. He, J. Dong, Y. Du, Curricular contrastive regularization for physics-aware single image dehazing, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2023, pp. 5785\u20135794.","DOI":"10.1109\/CVPR52729.2023.00560"},{"key":"10.1016\/j.knosys.2026.115994_b9","unstructured":"E. Deniz, G. Anil, K.E. Hazim, Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing, in: IEEE Conf. Comput. Vis. Pattern Recog. Worksh., 2018, pp. 825\u2013833."},{"key":"10.1016\/j.knosys.2026.115994_b10","series-title":"IEEE Int. Conf. Image Process.","first-page":"963","article-title":"Unsupervised conditional disentangle network for image dehazing","author":"Jin","year":"2020"},{"key":"10.1016\/j.knosys.2026.115994_b11","doi-asserted-by":"crossref","unstructured":"A. Dudhane, S. Murala, CDNet: Single Image De-Hazing Using Unpaired Adversarial Training, in: IEEE Winter Conf. Appl. Comput. Vis., 2019, pp. 1147\u20131155.","DOI":"10.1109\/WACV.2019.00127"},{"key":"10.1016\/j.knosys.2026.115994_b12","series-title":"IEEE Int. Conf. Artificial Intell. and Comput. Appl.","first-page":"934","article-title":"Research on single image dehazing enhancement method based on cyclegan","author":"Wang","year":"2020"},{"key":"10.1016\/j.knosys.2026.115994_b13","series-title":"Proc. IEEE Eur. Conf. Comput. Vis.","first-page":"632","article-title":"Unpaired deep image dehazing using contrastive disentanglement learning","author":"Chen","year":"2022"},{"key":"10.1016\/j.knosys.2026.115994_b14","first-page":"3391","article-title":"RefineDNet: A weakly supervised refinement framework for single image dehazing","volume":"30","author":"Zhao","year":"2021","journal-title":"TIP"},{"key":"10.1016\/j.knosys.2026.115994_b15","doi-asserted-by":"crossref","unstructured":"Y. Yang, C. Wang, R. Liu, L. Zhang, X. Guo, D. Tao, Self-augmented unpaired image dehazing via density and depth decomposition, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2022, pp. 2037\u20132046.","DOI":"10.1109\/CVPR52688.2022.00208"},{"key":"10.1016\/j.knosys.2026.115994_b16","first-page":"1361","article-title":"UCL-dehaze: Toward real-world image dehazing via unsupervised contrastive learning","volume":"33","author":"Wang","year":"2024","journal-title":"TIP"},{"key":"10.1016\/j.knosys.2026.115994_b17","article-title":"Reference-based image dehazing with internal and external contrastive learning","author":"Liu","year":"2023","journal-title":"TCSVT"},{"key":"10.1016\/j.knosys.2026.115994_b18","series-title":"Int. Conf. Mach. Learn.","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.knosys.2026.115994_b19","doi-asserted-by":"crossref","unstructured":"R. Abdelfattah, Q. Guo, X. Li, X. Wang, S. Wang, Cdul: Clip-driven unsupervised learning for multi-label image classification, in: Proc. IEEE Int. Conf. Comput. Vis., 2023, pp. 1348\u20131357.","DOI":"10.1109\/ICCV51070.2023.00130"},{"key":"10.1016\/j.knosys.2026.115994_b20","series-title":"Cma-clip: Cross-modality attention clip for image-text classification","author":"Liu","year":"2021"},{"key":"10.1016\/j.knosys.2026.115994_b21","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recog.","first-page":"11686","article-title":"Cris: Clip-driven referring image segmentation","author":"Wang","year":"2022"},{"key":"10.1016\/j.knosys.2026.115994_b22","doi-asserted-by":"crossref","unstructured":"Z. Zhou, Y. Lei, B. Zhang, L. Liu, Y. Liu, Zegclip: Towards adapting clip for zero-shot semantic segmentation, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2023, pp. 11175\u201311185.","DOI":"10.1109\/CVPR52729.2023.01075"},{"key":"10.1016\/j.knosys.2026.115994_b23","doi-asserted-by":"crossref","unstructured":"S. Yang, M. Ding, Y. Wu, Z. Li, J. Zhang, Implicit Neural Representation for Cooperative Low-light Image Enhancement, in: Proc. IEEE Int. Conf. Comput. Vis., 2023, pp. 12918\u201312927.","DOI":"10.1109\/ICCV51070.2023.01187"},{"key":"10.1016\/j.knosys.2026.115994_b24","series-title":"RAVE: Residual vector embedding for CLIP-guided backlit image enhancement","author":"Gaintseva","year":"2024"},{"key":"10.1016\/j.knosys.2026.115994_b25","doi-asserted-by":"crossref","unstructured":"Z. Liang, C. Li, S. Zhou, R. Feng, C.C. Loy, Iterative prompt learning for unsupervised backlit image enhancement, in: Proc. IEEE Int. Conf. Comput. Vis., 2023, pp. 8094\u20138103.","DOI":"10.1109\/ICCV51070.2023.00743"},{"key":"10.1016\/j.knosys.2026.115994_b26","doi-asserted-by":"crossref","unstructured":"K. He, J. Sun, X. Tang, Single image haze removal using dark channel prior, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2009, pp. 1956\u20131963.","DOI":"10.1109\/CVPR.2009.5206515"},{"key":"10.1016\/j.knosys.2026.115994_b27","first-page":"3522","article-title":"A fast single image haze removal algorithm using color attenuation prior","author":"Zhu","year":"2015","journal-title":"TIP"},{"key":"10.1016\/j.knosys.2026.115994_b28","doi-asserted-by":"crossref","unstructured":"Z. Chen, Y. Wang, Y. Yang, D. Liu, PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2021, pp. 7180\u20137189.","DOI":"10.1109\/CVPR46437.2021.00710"},{"issue":"2","key":"10.1016\/j.knosys.2026.115994_b29","first-page":"575","article-title":"Wsamf-net: Wavelet spatial attention-based multistream feedback network for single image dehazing","volume":"33","author":"Song","year":"2022","journal-title":"TCSVT"},{"key":"10.1016\/j.knosys.2026.115994_b30","first-page":"8457","article-title":"Zero-shot image dehazing","author":"Li","year":"2020","journal-title":"TIP"},{"key":"10.1016\/j.knosys.2026.115994_b31","doi-asserted-by":"crossref","unstructured":"B. Li, Y. Gou, S. Gu, J.Z. Liu, J.T. Zhou, X. Peng, You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network, in: Int. J. Comput. Vis., 2021, pp. 1\u201314.","DOI":"10.1007\/s11263-021-01431-5"},{"key":"10.1016\/j.knosys.2026.115994_b32","first-page":"2766","article-title":"Semi-supervised image dehazing","volume":"29","author":"Li","year":"2019","journal-title":"TIP"},{"key":"10.1016\/j.knosys.2026.115994_b33","series-title":"Non-aligned supervision for real image dehazing","author":"Fan","year":"2023"},{"key":"10.1016\/j.knosys.2026.115994_b34","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"2242","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu","year":"2017"},{"key":"10.1016\/j.knosys.2026.115994_b35","series-title":"Int. Conf. Mach. Learn.","first-page":"12888","article-title":"Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation","author":"Li","year":"2022"},{"key":"10.1016\/j.knosys.2026.115994_b36","doi-asserted-by":"crossref","unstructured":"K. Zhou, J. Yang, C.C. Loy, Z. Liu, Conditional prompt learning for vision-language models, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2022, pp. 16816\u201316825.","DOI":"10.1109\/CVPR52688.2022.01631"},{"issue":"9","key":"10.1016\/j.knosys.2026.115994_b37","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","article-title":"Learning to prompt for vision-language models","volume":"130","author":"Zhou","year":"2022","journal-title":"IJCV"},{"key":"10.1016\/j.knosys.2026.115994_b38","doi-asserted-by":"crossref","unstructured":"X. Dong, J. Bao, Y. Zheng, T. Zhang, D. Chen, H. Yang, M. Zeng, W. Zhang, L. Yuan, D. Chen, F. Wen, N. Yu, MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2023, pp. 10995\u201311005.","DOI":"10.1109\/CVPR52729.2023.01058"},{"key":"10.1016\/j.knosys.2026.115994_b39","unstructured":"D. Kim, K. Yang, T.Y. Liu, J. Deng, F-VLM: Open-Vocabulary Object Detection upon Frozen Vision-Language Models, in: Int. Conf. Learn. Represent., 2023."},{"key":"10.1016\/j.knosys.2026.115994_b40","first-page":"581","article-title":"CLIP-adapter: Better vision-language models with feature adapters","volume":"vol. 132","author":"Gao","year":"2023"},{"key":"10.1016\/j.knosys.2026.115994_b41","series-title":"Proc. IEEE Eur. Conf. Comput. Vis.","first-page":"493","article-title":"Tip-adapter: Training-free adaption of CLIP for few-shot classification","author":"Zhang","year":"2022"},{"key":"10.1016\/j.knosys.2026.115994_b42","doi-asserted-by":"crossref","unstructured":"S. Wang, C. Saharia, C. Montgomery, J. Pont-Tuset, S. Noy, S. Pellegrini, Y. Onoe, S. Laszlo, D.J. Fleet, R. Soricut, J. Baldridge, M. Norouzi, P. Anderson, W. Chan, Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2023, pp. 18359\u201318369.","DOI":"10.1109\/CVPR52729.2023.01761"},{"key":"10.1016\/j.knosys.2026.115994_b43","series-title":"Int. Conf. Mach. Learn.","first-page":"8821","article-title":"Zero-shot text-to-image generation","author":"Ramesh","year":"2021"},{"key":"10.1016\/j.knosys.2026.115994_b44","series-title":"Hierarchical text-conditional image generation with clip latents","first-page":"3","author":"Ramesh","year":"2022"},{"key":"10.1016\/j.knosys.2026.115994_b45","doi-asserted-by":"crossref","unstructured":"R. Rombach, A. Blattmann, D. Lorenz, P. Esser, B. Ommer, High-resolution image synthesis with latent diffusion models, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2022, pp. 10684\u201310695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10.1016\/j.knosys.2026.115994_b46","doi-asserted-by":"crossref","unstructured":"P. Isola, J.Y. Zhu, T. Zhou, A.A. Efros, Image-to-image translation with conditional adversarial networks, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2017, pp. 1125\u20131134.","DOI":"10.1109\/CVPR.2017.632"},{"key":"10.1016\/j.knosys.2026.115994_b47","doi-asserted-by":"crossref","unstructured":"T. Park, A.A. Efros, R. Zhang, J.Y. Zhu, Contrastive learning for unpaired image-to-image translation, in: Proc. IEEE Eur. Conf. Comput. Vis., 2020, pp. 319\u2013345.","DOI":"10.1007\/978-3-030-58545-7_19"},{"key":"10.1016\/j.knosys.2026.115994_b48","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.knosys.2026.115994_b49","doi-asserted-by":"crossref","unstructured":"J. Deng, W. Dong, R. Socher, L.J. Li, K. Li, L. Fei Fei, Imagenet: A large-scale hierarchical image database, in: Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2009, pp. 248\u2013255.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10.1016\/j.knosys.2026.115994_b50","doi-asserted-by":"crossref","unstructured":"X. Huang, S. Belongie, Arbitrary Style Transfer in Real-Time With Adaptive Instance Normalization, in: Proc. IEEE Int. Conf. Comput. Vis., 2017.","DOI":"10.1109\/ICCV.2017.167"},{"key":"10.1016\/j.knosys.2026.115994_b51","series-title":"Demystifying neural style transfer","author":"Li","year":"2017"},{"key":"10.1016\/j.knosys.2026.115994_b52","series-title":"Controlling vision-language models for universal image restoration","author":"Luo","year":"2023"},{"key":"10.1016\/j.knosys.2026.115994_b53","unstructured":"H. Chen, L. Zhao, Z. Wang, Z.H. Ming, Z. Zuo, A. Li, W. Xing, D. Lu, Artistic style transfer with internal-external learning and contrastive learning, in: Proc. Adv. Neural Inf. Process. Syst., 2021, pp. 26561\u201326573."},{"key":"10.1016\/j.knosys.2026.115994_b54","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.neucom.2020.10.061","article-title":"Prior guided conditional generative adversarial network for single image dehazing","volume":"423","author":"Su","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.115994_b55","first-page":"5492","article-title":"Real scene single image dehazing network with multi-prior guidance and domain transfer","volume":"27","author":"Su","year":"2025","journal-title":"TMM"},{"key":"10.1016\/j.knosys.2026.115994_b56","doi-asserted-by":"crossref","unstructured":"C.O. Ancuti, C. Ancuti, R. Timofte, C. De Vleeschouwer, O-haze: a dehazing benchmark with real hazy and haze-free outdoor images, in: IEEE Conf. Comput. Vis. Pattern Recog. Worksh., 2018, pp. 754\u2013762.","DOI":"10.1007\/978-3-030-01449-0_52"},{"key":"10.1016\/j.knosys.2026.115994_b57","doi-asserted-by":"crossref","unstructured":"C.O. Ancuti, C. Ancuti, R. Timofte, NH-HAZE: An image dehazing benchmark with non-homogeneous hazy and haze-free images, in: IEEE Conf. Comput. Vis. Pattern Recog. Worksh., 2020, pp. 444\u2013445.","DOI":"10.1109\/CVPRW50498.2020.00230"},{"key":"10.1016\/j.knosys.2026.115994_b58","series-title":"Int. Conf. Mach. Learn.","first-page":"1840","article-title":"Evaluation of defogging: A real-world benchmark dataset, a new criterion and baselines","author":"Zhao","year":"2019"},{"key":"10.1016\/j.knosys.2026.115994_b59","doi-asserted-by":"crossref","unstructured":"J. Li, Z. Feng, Q. She, H. Ding, C. Wang, G.H. Lee, Mine: Towards continuous depth mpi with nerf for novel view synthesis, in: Proc. IEEE Int. Conf. Comput. Vis., 2021, pp. 12578\u201312588.","DOI":"10.1109\/ICCV48922.2021.01235"},{"key":"10.1016\/j.knosys.2026.115994_b60","first-page":"492","article-title":"Benchmarking single-image dehazing and beyond","author":"Li","year":"2018","journal-title":"TIP"},{"issue":"4","key":"10.1016\/j.knosys.2026.115994_b61","first-page":"600","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"TIP"},{"key":"10.1016\/j.knosys.2026.115994_b62","first-page":"2555","article-title":"Exploring clip for assessing the look and feel of images","volume":"vol. 37","author":"Wang","year":"2023"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007203?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007203?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:14:06Z","timestamp":1781223246000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126007203"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":62,"alternative-id":["S0950705126007203"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115994","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dual-model joint prompting for image dehazing","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115994","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115994"}}