{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T12:11:11Z","timestamp":1770898271391,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China,China","doi-asserted-by":"crossref","award":["52025111"],"award-info":[{"award-number":["52025111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00371-024-03533-w","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T14:03:03Z","timestamp":1720015383000},"page":"2285-2297","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Transmission-guided multi-feature fusion Dehaze network"],"prefix":"10.1007","volume":"41","author":[{"given":"Xiaoyang","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongchao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongde","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongben","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"3533_CR1","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement"},{"key":"3533_CR2","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, Venice, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"3533_CR3","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.-Y., Le, Q.V.: NAS-FPN: learning scalable feature pyramid architecture for object detection. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Long Beach, CA, USA, pp. 7029\u20137038 (2019)","DOI":"10.1109\/CVPR.2019.00720"},{"key":"3533_CR4","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640\u2013651 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3533_CR5","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40, 834\u2013848 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3533_CR6","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., et al.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Honolulu, HI, pp. 6230\u20136239 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"3533_CR7","doi-asserted-by":"crossref","unstructured":"Kendall, A., Martirosyan, H., Dasgupta, S., et al.: End-to-end learning of geometry and context for deep stereo regression. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, Venice, pp. 66\u201375 (2017)","DOI":"10.1109\/ICCV.2017.17"},{"key":"3533_CR8","doi-asserted-by":"crossref","unstructured":"Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Salt Lake City, UT, pp. 5410\u20135418 (2018)","DOI":"10.1109\/CVPR.2018.00567"},{"key":"3533_CR9","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, K., Yang, W., et al.: Group-wise correlation stereo network. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Long Beach, CA, USA, pp. 3268\u20133277 (2019)","DOI":"10.1109\/CVPR.2019.00339"},{"key":"3533_CR10","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1109\/JQE.1978.1069864","volume":"14","author":"A Cantor","year":"1978","unstructured":"Cantor, A.: Optics of the atmosphere\u2013Scattering by molecules and particles. IEEE J. Quantum Electron. 14, 698\u2013699 (1978). https:\/\/doi.org\/10.1109\/JQE.1978.1069864","journal-title":"IEEE J. Quantum Electron."},{"key":"3533_CR11","first-page":"1","volume-title":"ACM SIGGRAPH ASIA 2008 courses on-SIGGRAPH Asia \u201908","author":"SG Narasimhan","year":"2008","unstructured":"Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. In: ACM SIGGRAPH ASIA 2008 courses on-SIGGRAPH Asia \u201908, pp. 1\u201322. ACM Press, Singapore (2008)"},{"key":"3533_CR12","doi-asserted-by":"crossref","unstructured":"Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE, Kerkyra, Greece, vol. 2D, pp. 820\u2013827 (1999)","DOI":"10.1109\/ICCV.1999.790306"},{"key":"3533_CR13","doi-asserted-by":"crossref","unstructured":"Berman, D., Treibitz, T., Avidan, S.: Non-local Image Dehazing. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, NV, USA, pp. 1674\u20131682 (2016)","DOI":"10.1109\/CVPR.2016.185"},{"key":"3533_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2651362","volume":"34","author":"R Fattal","year":"2014","unstructured":"Fattal, R.: Dehazing using color-lines. ACM Trans. Gr. 34, 1\u201314 (2014). https:\/\/doi.org\/10.1145\/2651362","journal-title":"ACM Trans. Gr."},{"key":"3533_CR15","doi-asserted-by":"crossref","unstructured":"Kaiming, H., Jian, S., Xiaoou, T.: Single image haze removal using dark channel prior. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Miami, FL, pp. 1956\u20131963 (2009)","DOI":"10.1109\/CVPR.2009.5206515"},{"key":"3533_CR16","doi-asserted-by":"publisher","first-page":"3522","DOI":"10.1109\/TIP.2015.2446191","volume":"24","author":"Q Zhu","year":"2015","unstructured":"Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24, 3522\u20133533 (2015). https:\/\/doi.org\/10.1109\/TIP.2015.2446191","journal-title":"IEEE Trans. Image Process."},{"key":"3533_CR17","doi-asserted-by":"publisher","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","volume":"25","author":"B Cai","year":"2016","unstructured":"Cai, B., Xu, X., Jia, K., et al.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25, 5187\u20135198 (2016). https:\/\/doi.org\/10.1109\/TIP.2016.2598681","journal-title":"IEEE Trans. Image Process."},{"key":"3533_CR18","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., et al.: AOD-Net: all-in-one Dehazing network. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, Venice, pp. 4780\u20134788 (2017)","DOI":"10.1109\/ICCV.2017.511"},{"key":"3533_CR19","doi-asserted-by":"crossref","unstructured":"Liu, X., Ma, Y., Shi, Z., Chen, J.: GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV). IEEE, Seoul, Korea (South), pp. 7313\u20137322 (2019)","DOI":"10.1109\/ICCV.2019.00741"},{"key":"3533_CR20","doi-asserted-by":"crossref","unstructured":"Ren, W., Ma, L., Zhang, J., et al.: Gated fusion network for single image Dehazing. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Salt Lake City, UT, pp. 3253\u20133261 (2018)","DOI":"10.1109\/CVPR.2018.00343"},{"key":"3533_CR21","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3533_CR22","doi-asserted-by":"publisher","first-page":"11908","DOI":"10.1609\/aaai.v34i07.6865","volume":"34","author":"X Qin","year":"2020","unstructured":"Qin, X., Wang, Z., Bai, Y., et al.: FFA-net: feature fusion attention network for single image Dehazing. AAAI 34, 11908\u201311915 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.6865","journal-title":"AAAI"},{"key":"3533_CR23","doi-asserted-by":"crossref","unstructured":"Chen, D., He, M., Fan, Q., et al.: Gated context aggregation network for image Dehazing and Deraining. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, Waikoloa Village, HI, USA, pp. 1375\u20131383 (2019)","DOI":"10.1109\/WACV.2019.00151"},{"key":"3533_CR24","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Honolulu, HI, pp. 2790\u20132798 (2017)","DOI":"10.1109\/CVPR.2017.298"},{"key":"3533_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, NV, USA, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3533_CR26","doi-asserted-by":"crossref","unstructured":"Wu, H., Qu, Y., Lin, S., et al.: Contrastive learning for compact single image Dehazing. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Nashville, TN, USA, pp. 10546\u201310555 (2021)","DOI":"10.1109\/CVPR46437.2021.01041"},{"key":"3533_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1360612.1360671","volume":"27","author":"R Fattal","year":"2008","unstructured":"Fattal, R.: Single image dehazing. ACM Trans. Gr. 27, 1\u20139 (2008). https:\/\/doi.org\/10.1145\/1360612.1360671","journal-title":"ACM Trans. Gr."},{"key":"3533_CR28","doi-asserted-by":"publisher","first-page":"2357","DOI":"10.1109\/TIP.2018.2885490","volume":"28","author":"S Salazar-Colores","year":"2019","unstructured":"Salazar-Colores, S., Cabal-Yepez, E., Ramos-Arreguin, J.M., et al.: A fast image Dehazing algorithm using morphological reconstruction. IEEE Trans. Image Process. 28, 2357\u20132366 (2019). https:\/\/doi.org\/10.1109\/TIP.2018.2885490","journal-title":"IEEE Trans. Image Process."},{"key":"3533_CR29","doi-asserted-by":"crossref","unstructured":"Meng, G., Wang, Y., Duan, J., et al.: Efficient image Dehazing with boundary constraint and contextual regularization. In: 2013 IEEE International Conference on Computer Vision. IEEE, Sydney, Australia, pp. 617\u2013624 (2013)","DOI":"10.1109\/ICCV.2013.82"},{"key":"3533_CR30","doi-asserted-by":"publisher","first-page":"5178","DOI":"10.1109\/TIP.2018.2849928","volume":"27","author":"Q Liu","year":"2018","unstructured":"Liu, Q., Gao, X., He, L., Lu, W.: Single image Dehazing with depth-aware non-local total variation regularization. IEEE Trans. Image Process. 27, 5178\u20135191 (2018). https:\/\/doi.org\/10.1109\/TIP.2018.2849928","journal-title":"IEEE Trans. Image Process."},{"key":"3533_CR31","doi-asserted-by":"crossref","unstructured":"Dong, H., Pan, J., Xiang, L., et al.: Multi-scale boosted Dehazing network with dense feature fusion. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Seattle, WA, USA, pp. 2154\u20132164 (2020)","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"3533_CR32","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/s11263-019-01235-8","volume":"128","author":"W Ren","year":"2020","unstructured":"Ren, W., Pan, J., Zhang, H., et al.: Single image Dehazing via multi-scale convolutional neural networks with holistic edges. Int. J. Comput. Vis. 128, 240\u2013259 (2020). https:\/\/doi.org\/10.1007\/s11263-019-01235-8","journal-title":"Int. J. Comput. Vis."},{"key":"3533_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, H., Patel, V.M.: Densely connected pyramid Dehazing network. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Salt Lake City, UT, USA, pp. 3194\u20133203 (2018)","DOI":"10.1109\/CVPR.2018.00337"},{"key":"3533_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, D., Wang, X.: Dynamic multi-scale network for dual-pixel images defocus deblurring with transformer. In: 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, Taipei, Taiwan, pp. 1\u20136 (2022)","DOI":"10.1109\/ICME52920.2022.9859631"},{"key":"3533_CR35","doi-asserted-by":"crossref","unstructured":"Lu, L., Xiong, Q., Chu, D., Xu, B.: MixDehazeNet: Mix Structure Block for Image Dehazing Network (2023)","DOI":"10.1109\/IJCNN60899.2024.10651326"},{"key":"3533_CR36","doi-asserted-by":"publisher","first-page":"3391","DOI":"10.1109\/TIP.2021.3060873","volume":"30","author":"S Zhao","year":"2021","unstructured":"Zhao, S., Zhang, L., Shen, Y., Zhou, Y.: RefineDNet: a weakly supervised refinement framework for single image Dehazing. IEEE Trans. Image Process. 30, 3391\u20133404 (2021). https:\/\/doi.org\/10.1109\/TIP.2021.3060873","journal-title":"IEEE Trans. Image Process."},{"key":"3533_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNNLS.2022.3184164","volume":"5","author":"G Fan","year":"2022","unstructured":"Fan, G., Gan, M., Fan, B., Chen, C.L.P.: Multiscale cross-connected Dehazing network with scene depth fusion. IEEE Trans. Neural Netw. Learn. Syst. 5, 1\u201315 (2022). https:\/\/doi.org\/10.1109\/TNNLS.2022.3184164","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3533_CR38","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, L., Pei, S., et al.: From synthetic to real: image Dehazing collaborating with unlabeled real data. In: Proceedings of the 29th ACM International Conference on Multimedia. ACM, Virtual Event China, pp. 50\u201358 (2021)","DOI":"10.1145\/3474085.3475331"},{"key":"3533_CR39","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","volume":"28","author":"B Li","year":"2019","unstructured":"Li, B., Ren, W., Fu, D., et al.: Benchmarking single-image Dehazing and beyond. IEEE Trans. Image Process. 28, 492\u2013505 (2019). https:\/\/doi.org\/10.1109\/TIP.2018.2867951","journal-title":"IEEE Trans. Image Process."},{"key":"3533_CR40","doi-asserted-by":"crossref","unstructured":"Ye, T., Jiang, M., Zhang, Y., et al.: Perceiving and Modeling Density is All You Need for Image Dehazing (2021)","DOI":"10.1007\/978-3-031-19800-7_8"},{"key":"3533_CR41","doi-asserted-by":"publisher","first-page":"2856","DOI":"10.1109\/TIP.2018.2813092","volume":"27","author":"Y-T Peng","year":"2018","unstructured":"Peng, Y.-T., Cao, K., Cosman, P.C.: Generalization of the dark channel prior for single image restoration. IEEE Trans. Image Process. 27, 2856\u20132868 (2018). https:\/\/doi.org\/10.1109\/TIP.2018.2813092","journal-title":"IEEE Trans. Image Process."},{"key":"3533_CR42","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/j.neucom.2020.10.061","volume":"423","author":"YZ Su","year":"2021","unstructured":"Su, Y.Z., Cui, Z.G., He, C., et al.: Prior guided conditional generative adversarial network for single image dehazing. Neurocomputing 423, 620\u2013638 (2021). https:\/\/doi.org\/10.1016\/j.neucom.2020.10.061","journal-title":"Neurocomputing"},{"key":"3533_CR43","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2019.09.094","volume":"378","author":"F Guo","year":"2020","unstructured":"Guo, F., Zhao, X., Tang, J., et al.: Single image dehazing based on fusion strategy. Neurocomputing 378, 9\u201323 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.09.094","journal-title":"Neurocomputing"},{"key":"3533_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-023-03177-2","author":"P Liu","year":"2023","unstructured":"Liu, P., Liu, J.: Knowledge-guided multi-perception attention network for image dehazing. Vis. Comput. (2023). https:\/\/doi.org\/10.1007\/s00371-023-03177-2","journal-title":"Vis. Comput."},{"key":"3533_CR45","doi-asserted-by":"crossref","unstructured":"Jose, V.J.M., Yasarla. R., Patel, V.M.: TransWeather: transformer-based restoration of images degraded by adverse weather conditions. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, New Orleans, LA, USA, pp. 2343\u20132353 (2022)","DOI":"10.1109\/CVPR52688.2022.00239"},{"key":"3533_CR46","doi-asserted-by":"publisher","first-page":"11367","DOI":"10.1007\/s11042-023-15844-6","volume":"83","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Xu, T., Tian, K.: PSPAN:pyramid spatially weighted pixel attention network for image dehazing. Multimed. Tools Appl. 83, 11367\u201311385 (2024). https:\/\/doi.org\/10.1007\/s11042-023-15844-6","journal-title":"Multimed. Tools Appl."},{"key":"3533_CR47","doi-asserted-by":"crossref","unstructured":"Qu, Y., Chen, Y., Huang, J., Xie, Y.: Enhanced Pix2pix Dehazing network. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Long Beach, CA, USA, pp. 8152\u20138160 (2019)","DOI":"10.1109\/CVPR.2019.00835"},{"key":"3533_CR48","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1109\/TCSVT.2019.2901629","volume":"30","author":"B Sheng","year":"2020","unstructured":"Sheng, B., Li, P., Fang, X., et al.: Depth-aware motion deblurring using loopy belief propagation. IEEE Trans. Circuits Syst. Video Technol. 30, 955\u2013969 (2020). https:\/\/doi.org\/10.1109\/TCSVT.2019.2901629","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3533_CR49","doi-asserted-by":"publisher","first-page":"6142","DOI":"10.1109\/TIP.2021.3092814","volume":"30","author":"Y Wen","year":"2021","unstructured":"Wen, Y., Chen, J., Sheng, B., et al.: Structure-aware motion Deblurring using multi-adversarial optimized CycleGAN. IEEE Trans. Image Process. 30, 6142\u20136155 (2021). https:\/\/doi.org\/10.1109\/TIP.2021.3092814","journal-title":"IEEE Trans. Image Process."},{"key":"3533_CR50","doi-asserted-by":"publisher","first-page":"7719","DOI":"10.1109\/TNNLS.2022.3146004","volume":"34","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y., Chen, Z., Li, P., et al.: FSAD-net: feedback spatial attention Dehazing network. IEEE Trans. Neural Netw. Learn. Syst. 34, 7719\u20137733 (2023). https:\/\/doi.org\/10.1109\/TNNLS.2022.3146004","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3533_CR51","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhan, J., He, S., et al.: Curricular contrastive regularization for physics-aware single image Dehazing. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Vancouver, BC, Canada, pp. 5785\u20135794 (2023)","DOI":"10.1109\/CVPR52729.2023.00560"},{"key":"3533_CR52","doi-asserted-by":"crossref","unstructured":"Wu, R.-Q., Duan, Z.-P., Guo, C.-L., et al.: RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Vancouver, BC, Canada, pp. 22282\u201322291 (2023)","DOI":"10.1109\/CVPR52729.2023.02134"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03533-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03533-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03533-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T11:29:52Z","timestamp":1741001392000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03533-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,3]]},"references-count":52,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3533"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03533-w","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,3]]},"assertion":[{"value":"31 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing fnancial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The data collected come from a publicrepository. They are permitted and don't violate any ethical rules.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}