{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:42:47Z","timestamp":1775068967333,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s10489-022-03540-1","type":"journal-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T14:18:58Z","timestamp":1651760338000},"page":"2147-2160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MsDA: Multi-scale domain adaptation dehazing network"],"prefix":"10.1007","volume":"53","author":[{"given":"Hu","family":"Yu","sequence":"first","affiliation":[]},{"given":"Xiaopeng","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4973-6444","authenticated-orcid":false,"given":"Cien","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Lian","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Yuanmei","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"issue":"3","key":"3540_CR1","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1109\/TPAMI.2018.2882478","volume":"42","author":"D Berman","year":"2018","unstructured":"Berman D, Treibitz T, Avidan S (2018) Single image dehazing using haze-lines. IEEE Trans Pattern Anal Machine Intell 42(3):720\u2013734","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"issue":"11","key":"3540_CR2","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, Qing C, Tao D (2016) Dehazenet: An end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187\u20135198","journal-title":"IEEE Trans Image Process"},{"key":"3540_CR3","unstructured":"Chang CM, Sung CS, Lin TN (2021) Damix: Density-aware data augmentation for unsupervised domain adaptation on single image dehazing. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/210912544"},{"key":"3540_CR4","doi-asserted-by":"crossref","unstructured":"Chen Z, Wang Y, Yang Y, Liu D (2021) Psd: Principled synthetic-to-real dehazing guided by physical priors. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7180\u20137189","DOI":"10.1109\/CVPR46437.2021.00710"},{"issue":"11","key":"3540_CR5","doi-asserted-by":"publisher","first-page":"3888","DOI":"10.1109\/TIP.2015.2456502","volume":"24","author":"LK Choi","year":"2015","unstructured":"Choi L K, You J, Bovik A C (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24(11):3888\u20133901","journal-title":"IEEE Trans Image Process"},{"key":"3540_CR6","doi-asserted-by":"crossref","unstructured":"Dong H, Pan J, Xiang L, Hu Z, Zhang X, Wang F, Yang MH (2020) Multi-scale boosted dehazing network with dense feature fusion. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2157\u20132167","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"3540_CR7","doi-asserted-by":"crossref","unstructured":"Dong J, Pan J (2020) Physics-based feature dehazing networks. In: European conference on computer vision, Springer, pp 188\u2013204","DOI":"10.1007\/978-3-030-58577-8_12"},{"issue":"3","key":"3540_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1360612.1360671","volume":"27","author":"R Fattal","year":"2008","unstructured":"Fattal R (2008) Single image dehazing. ACM Trans Graph (TOG) 27(3):1\u20139","journal-title":"ACM Trans Graph (TOG)"},{"key":"3540_CR9","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International conference on machine learning, PMLR, pp 1180\u20131189"},{"key":"3540_CR10","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1109\/TIP.2019.2952032","volume":"29","author":"A Golts","year":"2019","unstructured":"Golts A, Freedman D, Elad M (2019) Unsupervised single image dehazing using dark channel prior loss. IEEE Trans Image Process 29:2692\u20132701","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"3540_CR11","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"3540_CR12","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2018) Deep back-projection networks for super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1664\u20131673","DOI":"10.1109\/CVPR.2018.00179"},{"issue":"12","key":"3540_CR13","first-page":"2341","volume":"33","author":"K He","year":"2010","unstructured":"He K, Sun J, Tang X (2010) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341\u20132353","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3540_CR14","unstructured":"Jiang L, Dai B, Wu W, Change Loy C (2020) Focal frequency loss for generative models. arXiv e-prints pp arXiv\u20132012"},{"key":"3540_CR15","doi-asserted-by":"crossref","unstructured":"Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision, Springer, pp 694\u2013711","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"3540_CR16","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/14126980"},{"key":"3540_CR17","doi-asserted-by":"crossref","unstructured":"Li B, Peng X, Wang Z, Xu J, Feng D (2017) Aod-net: All-in-one dehazing network. In: Proceedings of the IEEE international conference on computer vision, pp 4770\u20134778","DOI":"10.1109\/ICCV.2017.511"},{"issue":"1","key":"3540_CR18","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","volume":"28","author":"B Li","year":"2018","unstructured":"Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2018) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28(1):492\u2013505","journal-title":"IEEE Trans Image Process"},{"key":"3540_CR19","doi-asserted-by":"publisher","first-page":"2766","DOI":"10.1109\/TIP.2019.2952690","volume":"29","author":"L Li","year":"2019","unstructured":"Li L, Dong Y, Ren W, Pan J, Gao C, Sang N, Yang M H (2019) Semi-supervised image dehazing. IEEE Trans Image Process 29:2766\u20132779","journal-title":"IEEE Trans Image Process"},{"key":"3540_CR20","doi-asserted-by":"crossref","unstructured":"Liang M, Yang B, Wang S, Urtasun R (2018) Deep continuous fusion for multi-sensor 3d object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 641\u2013656","DOI":"10.1007\/978-3-030-01270-0_39"},{"issue":"4","key":"3540_CR21","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1007\/s10489-017-0942-z","volume":"47","author":"HY Lin","year":"2017","unstructured":"Lin HY, Lin CJ (2017) Using a hybrid of fuzzy theory and neural network filter for single image dehazing. Appl Intell 47(4):1099\u20131114","journal-title":"Appl Intell"},{"key":"3540_CR22","doi-asserted-by":"crossref","unstructured":"Liu Y, Pan J, Ren J, Su Z (2019) Learning deep priors for image dehazing. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2492\u20132500","DOI":"10.1109\/ICCV.2019.00258"},{"key":"3540_CR23","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhu L, Pei S, Fu H, Qin J, Zhang Q, Wan L, Feng W (2021) From synthetic to real: Image dehazing collaborating with unlabeled real data. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/210802934","DOI":"10.1145\/3474085.3475331"},{"key":"3540_CR24","unstructured":"Mao XJ, Shen C, Yang YB (2016) Image restoration using convolutional auto-encoders with symmetric skip connections. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/160608921"},{"key":"3540_CR25","unstructured":"McCartney EJ (1976) Optics of the atmosphere: Scattering by molecules and particles. New York"},{"issue":"3","key":"3540_CR26","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","volume":"20","author":"A Mittal","year":"2012","unstructured":"Mittal A, Soundararajan R, Bovik A C (2012) Making a \u201ccompletely blind\u201d image quality analyzer. IEEE Signal Process Lett 20(3):209\u2013212","journal-title":"IEEE Signal Process Lett"},{"issue":"3","key":"3540_CR27","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1023\/A:1016328200723","volume":"48","author":"SG Narasimhan","year":"2002","unstructured":"Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. International Journal of Computer Vision 48(3):233\u2013254","journal-title":"International Journal of Computer Vision"},{"issue":"10","key":"3540_CR28","doi-asserted-by":"publisher","first-page":"e3","DOI":"10.23915\/distill.00003","volume":"1","author":"A Odena","year":"2016","unstructured":"Odena A, Dumoulin V, Olah C (2016) Deconvolution and checkerboard artifacts. Distill 1 (10):e3","journal-title":"Distill"},{"key":"3540_CR29","doi-asserted-by":"crossref","unstructured":"Qi CR, Liu W, Wu C, Su H, Guibas LJ (2018) Frustum pointnets for 3d object detection from rgb-d data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 918\u2013927","DOI":"10.1109\/CVPR.2018.00102"},{"key":"3540_CR30","doi-asserted-by":"crossref","unstructured":"Qu Y, Chen Y, Huang J, Xie Y (2019) Enhanced pix2pix dehazing network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8160\u20138168","DOI":"10.1109\/CVPR.2019.00835"},{"key":"3540_CR31","doi-asserted-by":"crossref","unstructured":"Ren W, Liu S, Zhang H, Pan J, Cao X, Yang MH (2016) Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision, Springer, pp 154\u2013169","DOI":"10.1007\/978-3-319-46475-6_10"},{"issue":"2","key":"3540_CR32","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1137\/140990978","volume":"8","author":"Y Romano","year":"2015","unstructured":"Romano Y, Elad M (2015) Boosting of image denoising algorithms. SIAM Journal on Imaging Sciences 8(2):1187\u20131219","journal-title":"SIAM Journal on Imaging Sciences"},{"issue":"1-4","key":"3540_CR33","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"LI Rudin","year":"1992","unstructured":"Rudin L I, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(1-4):259\u2013268","journal-title":"Physica D: Nonlinear Phenomena"},{"key":"3540_CR34","doi-asserted-by":"crossref","unstructured":"Shao Y, Li L, Ren W, Gao C, Sang N (2020) Domain adaptation for image dehazing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2808\u20132817","DOI":"10.1109\/CVPR42600.2020.00288"},{"key":"3540_CR35","doi-asserted-by":"crossref","unstructured":"Shyam P, Yoon KJ, Kim KS (2021) Towards domain invariant single image dehazing. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/210110449","DOI":"10.1609\/aaai.v35i11.17162"},{"key":"3540_CR36","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/14091556"},{"issue":"12","key":"3540_CR37","doi-asserted-by":"publisher","first-page":"4276","DOI":"10.1007\/s10489-019-01504-6","volume":"49","author":"D Singh","year":"2019","unstructured":"Singh D, Kumar V, Kaur M (2019) Single image dehazing using gradient channel prior. Appl Intell 49(12):4276\u20134293","journal-title":"Appl Intell"},{"key":"3540_CR38","doi-asserted-by":"crossref","unstructured":"Tan RT (2008) Visibility in bad weather from a single image. In: 2008 IEEE conference on computer vision and pattern recognition, IEEE, pp 1\u20138","DOI":"10.1109\/CVPR.2008.4587643"},{"issue":"5","key":"3540_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3400066","volume":"11","author":"G Wilson","year":"2020","unstructured":"Wilson G, Cook DJ (2020) A survey of unsupervised deep domain adaptation. ACM Trans Intell Syst Technol (TIST) 11(5):1\u201346","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"3540_CR40","doi-asserted-by":"crossref","unstructured":"Zhang H, Patel VM (2018) Densely connected pyramid dehazing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3194\u20133203","DOI":"10.1109\/CVPR.2018.00337"},{"key":"3540_CR41","doi-asserted-by":"crossref","unstructured":"Zhang J, Cao Y, Zha ZJ, Tao D (2020) Nighttime dehazing with a synthetic benchmark. In: Proceedings of the 28th ACM international conference on multimedia, pp 2355\u2013 2363","DOI":"10.1145\/3394171.3413763"},{"key":"3540_CR42","doi-asserted-by":"crossref","unstructured":"Zhang Y, Ding L, Sharma G (2017) Hazerd: An outdoor scene dataset and benchmark for single image dehazing. In: 2017 IEEE international conference on image processing (ICIP), IEEE, pp 3205\u20133209","DOI":"10.1109\/ICIP.2017.8296874"},{"key":"3540_CR43","unstructured":"Zhang Y, Zhang H, Deng B, Li S, Jia K, Zhang L (2021) Semi-supervised models are strong unsupervised domain adaptation learners. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/210600417"},{"key":"3540_CR44","doi-asserted-by":"crossref","unstructured":"Zheng Z, Ren W, Cao X, Hu X, Wang T, Song F, Jia X (2021) Ultra-high-definition image dehazing via multi-guided bilateral learning. In: 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), IEEE, pp 16180\u2013 16189","DOI":"10.1109\/CVPR46437.2021.01592"},{"key":"3540_CR45","doi-asserted-by":"crossref","unstructured":"Zhu Q, Mai J, Shao L (2014) Single image dehazing using color attenuation prior. In: BMVC, Citeseer","DOI":"10.5244\/C.28.114"},{"issue":"11","key":"3540_CR46","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 (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522\u20133533","journal-title":"IEEE Trans Image Process"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03540-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03540-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03540-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T04:53:08Z","timestamp":1672807988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03540-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,5]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["3540"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03540-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,5]]},"assertion":[{"value":"22 March 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}