{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T16:43:16Z","timestamp":1768408996812,"version":"3.49.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"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","doi-asserted-by":"publisher","award":["62272178"],"award-info":[{"award-number":["62272178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272178"],"award-info":[{"award-number":["62272178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272178"],"award-info":[{"award-number":["62272178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272178"],"award-info":[{"award-number":["62272178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272178"],"award-info":[{"award-number":["62272178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272178"],"award-info":[{"award-number":["62272178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272178"],"award-info":[{"award-number":["62272178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s11227-023-05286-0","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T19:33:54Z","timestamp":1682019234000},"page":"15729-15759","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MOONLIT: momentum-contrast and large-kernel for multi-fine-grained deraining"],"prefix":"10.1007","volume":"79","author":[{"given":"Yifan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jincai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanbo","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yugen","family":"Jian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"issue":"9","key":"5286_CR1","doi-asserted-by":"publisher","first-page":"6400","DOI":"10.1007\/s10489-021-02293-7","volume":"51","author":"SK Pal","year":"2021","unstructured":"Pal SK, Pramanik A, Maiti J, Mitra P (2021) Deep learning in multi-object detection and tracking: state of the art. App Intell 51(9):6400\u20136429","journal-title":"App Intell"},{"key":"5286_CR2","doi-asserted-by":"crossref","unstructured":"Lin G, Milan A, Shen C, Reid I (2017) Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1925\u20131934","DOI":"10.1109\/CVPR.2017.549"},{"key":"5286_CR3","doi-asserted-by":"crossref","unstructured":"Chen X, Pan J, Jiang K, Li Y, Huang Y, Kong C, Dai L, Fan Z (2022) Unpaired deep image deraining using dual contrastive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2017\u20132026","DOI":"10.1109\/CVPR52688.2022.00206"},{"key":"5286_CR4","doi-asserted-by":"crossref","unstructured":"Li M, Xie Q, Zhao Q, Wei W, Gu S, Tao J, Meng D (2018) Video rain streak removal by multiscale convolutional sparse coding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6644\u20136653","DOI":"10.1109\/CVPR.2018.00695"},{"key":"5286_CR5","doi-asserted-by":"crossref","unstructured":"Jiang K, Wang Z, Yi P, Chen C, Huang B, Luo Y, Ma J, Jiang J (2020) Multi-scale progressive fusion network for single image deraining. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 8346\u20138355","DOI":"10.1109\/CVPR42600.2020.00837"},{"key":"5286_CR6","doi-asserted-by":"publisher","first-page":"6288","DOI":"10.1109\/TIP.2020.2990606","volume":"29","author":"Y Du","year":"2020","unstructured":"Du Y, Xu J, Zhen X, Cheng M-M, Shao L (2020) Conditional variational image deraining. IEEE Tran Image Process 29:6288\u20136301","journal-title":"IEEE Tran Image Process"},{"key":"5286_CR7","doi-asserted-by":"crossref","unstructured":"Rai SN, Saluja R, Arora C, Balasubramanian VN, Subramanian A, Jawahar C (2022) Fluid: Few-shot self-supervised image deraining. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 3077\u20133086","DOI":"10.1109\/WACV51458.2022.00049"},{"key":"5286_CR8","doi-asserted-by":"crossref","unstructured":"Deng S, Wei M, Wang J, Feng Y, Liang L, Xie H, Wang FL, Wang M (2020) Detail-recovery image deraining via context aggregation networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 14560\u201314569","DOI":"10.1109\/CVPR42600.2020.01457"},{"key":"5286_CR9","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.neucom.2021.06.052","volume":"457","author":"P Wang","year":"2021","unstructured":"Wang P, Zhu H (2021) Single-image de-raining using joint filter and multi-scale deep alternate-connection dense network. Neurocomputing 457:306\u2013321","journal-title":"Neurocomputing"},{"key":"5286_CR10","doi-asserted-by":"crossref","unstructured":"Yasarla R, Sindagi VA, Patel VM (2020) Syn2real transfer learning for image deraining using gaussian processes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2726\u20132736","DOI":"10.1109\/CVPR42600.2020.00280"},{"key":"5286_CR11","doi-asserted-by":"crossref","unstructured":"Tan F, Qian Y, Kong Y, Zhang H, Zhou D, Fan Y, Chen L (2021) Dbswin: transformer based dual branch network for single image deraining. J Intell Fuzzy Syst (Preprint), 1\u201315","DOI":"10.2139\/ssrn.3993046"},{"key":"5286_CR12","doi-asserted-by":"publisher","first-page":"184841","DOI":"10.1109\/ACCESS.2020.3029857","volume":"8","author":"S Wang","year":"2020","unstructured":"Wang S, Liu Y, Qing Y, Wang C, Lan T, Yao R (2020) Detection of insulator defects with improved resnest and region proposal network. IEEE Access 8:184841\u2013184850. https:\/\/doi.org\/10.1109\/ACCESS.2020.3029857","journal-title":"IEEE Access"},{"issue":"18","key":"5286_CR13","doi-asserted-by":"publisher","first-page":"17483","DOI":"10.1109\/JSEN.2021.3098325","volume":"22","author":"H Liang","year":"2022","unstructured":"Liang H, Ji W, Wang R, Ma Y, Chen J, Chen M (2022) A scene-dependent sound event detection approach using multi-task learning. IEEE Sens J 22(18):17483\u201317489. https:\/\/doi.org\/10.1109\/JSEN.2021.3098325","journal-title":"IEEE Sens J"},{"key":"5286_CR14","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"5286_CR15","doi-asserted-by":"crossref","unstructured":"Yang W, Wang S, Xu D, Wang X, Liu J (2020) Towards scale-free rain streak removal via self-supervised fractal band learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 12629\u201312636","DOI":"10.1609\/aaai.v34i07.6954"},{"key":"5286_CR16","doi-asserted-by":"crossref","unstructured":"Wang C, Xing X, Wu Y, Su Z, Chen J (2020) Dcsfn: deep cross-scale fusion network for single image rain removal. In: Proceedings of the 28th ACM International Conference on Multimedia, pp 1643\u20131651","DOI":"10.1145\/3394171.3413820"},{"key":"5286_CR17","doi-asserted-by":"crossref","unstructured":"Chen C, Li H (2021) Robust representation learning with feedback for single image deraining. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7742\u20137751","DOI":"10.1109\/CVPR46437.2021.00765"},{"key":"5286_CR18","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.cag.2021.04.014","volume":"97","author":"X Lin","year":"2021","unstructured":"Lin X, Huang Q, Huang W, Tan X, Fang M, Ma L (2021) Single image deraining via detail-guided efficient channel attention network. Comput Graph 97:117\u2013125","journal-title":"Comput Graph"},{"key":"5286_CR19","doi-asserted-by":"crossref","unstructured":"Zhang J, Pan J, Ren J, Song Y, Bao L, Lau RW, Yang M-H (2018) Dynamic scene deblurring using spatially variant recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2521\u20132529","DOI":"10.1109\/CVPR.2018.00267"},{"issue":"7","key":"5286_CR20","doi-asserted-by":"publisher","first-page":"2480","DOI":"10.1109\/TPAMI.2020.2968521","volume":"43","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2020) Residual dense network for image restoration. IEEE Trans Pattern Anal Machine Intell 43(7):2480\u20132495","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"5286_CR21","unstructured":"Yuntong Y, Changfeng Y, Yi C, Lin Z, Xile Z, Luxin Y, Yonghong T (2022) Unsupervised deraining: where contrastive learning meets self-similarity. arXiv preprint arXiv:2203.11509"},{"key":"5286_CR22","doi-asserted-by":"crossref","unstructured":"Liu Y, Yue Z, Pan J, Su Z (2021) Unpaired learning for deep image deraining with rain direction regularizer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 4753\u20134761","DOI":"10.1109\/ICCV48922.2021.00471"},{"key":"5286_CR23","doi-asserted-by":"crossref","unstructured":"Zou W, Wang Y, Fu X, Cao Y (2022) Dreaming to prune image deraining networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6023\u20136032","DOI":"10.1109\/CVPR52688.2022.00593"},{"key":"5286_CR24","doi-asserted-by":"crossref","unstructured":"Yi Q, Li J, Dai Q, Fang F, Zhang G, Zeng T (2021) Structure-preserving deraining with residue channel prior guidance. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 4238\u20134247","DOI":"10.1109\/ICCV48922.2021.00420"},{"key":"5286_CR25","doi-asserted-by":"crossref","unstructured":"Xiao J, Zhou M, Fu X, Liu A, Zha Z-J (2021) Improving de-raining generalization via neural reorganization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 4987\u20134996","DOI":"10.1109\/ICCV48922.2021.00494"},{"issue":"11","key":"5286_CR26","doi-asserted-by":"publisher","first-page":"3943","DOI":"10.1109\/TCSVT.2019.2920407","volume":"30","author":"H Zhang","year":"2019","unstructured":"Zhang H, Sindagi V, Patel VM (2019) Image de-raining using a conditional generative adversarial network. IEEE Trans Circuits Syst Video Technol 30(11):3943\u20133956","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"5286_CR27","unstructured":"Mishra S, Shah A, Bansal A, Choi J, Shrivastava A, Sharma A, Jacobs D (2020) Learning visual representations for transfer learning by suppressing texture. arXiv preprint arXiv:2011.01901"},{"key":"5286_CR28","doi-asserted-by":"crossref","unstructured":"Ding X, Zhang X, Han J, Ding G (2022) Scaling up your kernels to 31x31: revisiting large kernel design in CNNS. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11963\u201311975","DOI":"10.1109\/CVPR52688.2022.01166"},{"key":"5286_CR29","doi-asserted-by":"crossref","unstructured":"Zhang H, Patel VM (2018) Density-aware single image de-raining using a multi-stream dense network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 695\u2013704","DOI":"10.1109\/CVPR.2018.00079"},{"key":"5286_CR30","doi-asserted-by":"crossref","unstructured":"Fu X, Huang J, Zeng D, Huang Y, Ding X, Paisley J (2017) Removing rain from single images via a deep detail network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3855\u20133863","DOI":"10.1109\/CVPR.2017.186"},{"key":"5286_CR31","doi-asserted-by":"crossref","unstructured":"Wang T, Yang X, Xu K, Chen S, Zhang Q, Lau RW (2019) Spatial attentive single-image deraining with a high quality real rain dataset. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 12270\u201312279","DOI":"10.1109\/CVPR.2019.01255"},{"key":"5286_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-022-04895-5","author":"H Yang","year":"2022","unstructured":"Yang H, Zhou D, Cao J, Zhao Q, Li M (2022) Rainformer: a pyramid transformer for single image deraining. J Supercomput. https:\/\/doi.org\/10.1007\/s11227-022-04895-5","journal-title":"J Supercomput"},{"key":"5286_CR33","doi-asserted-by":"publisher","first-page":"108909","DOI":"10.1016\/j.patcog.2022.108909","volume":"132","author":"H Shen","year":"2022","unstructured":"Shen H, Zhao Z-Q, Liao W, Tian W, Huang D-S (2022) Joint operation and attention block search for lightweight image restoration. Pattern Recognit 132:108909","journal-title":"Pattern Recognit"},{"key":"5286_CR34","doi-asserted-by":"publisher","first-page":"14013","DOI":"10.1007\/s00521-022-07226-0","volume":"34","author":"F Gao","year":"2022","unstructured":"Gao F, Mu X, Ouyang C, Yang K, Ji S, Guo J, Wei H, Wang N, Ma L, Yang B (2022) Mltdnet: an efficient multi-level transformer network for single image deraining. Neural Comput Appl 34:14013\u201314027","journal-title":"Neural Comput Appl"},{"key":"5286_CR35","doi-asserted-by":"publisher","first-page":"4544","DOI":"10.1109\/TIP.2020.2973802","volume":"29","author":"R Yasarla","year":"2020","unstructured":"Yasarla R, Patel VM (2020) Confidence measure guided single image de-raining. IEEE Trans Image Process 29:4544\u20134555","journal-title":"IEEE Trans Image Process"},{"key":"5286_CR36","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp 1597\u20131607. PMLR"},{"key":"5286_CR37","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9729\u20139738","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"5286_CR38","doi-asserted-by":"publisher","first-page":"116726","DOI":"10.1016\/j.image.2022.116726","volume":"106","author":"C Wang","year":"2022","unstructured":"Wang C, Shen Q, Wang X, Jiang G (2022) Momentum feature comparison network based on generative adversarial network for single image super-resolution. Signal Proces Image Commun 106:116726","journal-title":"Signal Proces Image Commun"},{"key":"5286_CR39","doi-asserted-by":"crossref","unstructured":"Li B, Liu X, Hu P, Wu Z, Lv J, Peng X (2022) All-in-one image restoration for unknown corruption. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 17452\u201317462","DOI":"10.1109\/CVPR52688.2022.01693"},{"key":"5286_CR40","doi-asserted-by":"crossref","unstructured":"Peng C, Zhang X, Yu G, Luo G, Sun J (2017) Large kernel matters\u2013improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4353\u20134361","DOI":"10.1109\/CVPR.2017.189"},{"key":"5286_CR41","doi-asserted-by":"publisher","first-page":"109376","DOI":"10.1016\/j.knosys.2022.109376","volume":"252","author":"H Feng","year":"2022","unstructured":"Feng H, Wang L, Li Y, Du A (2022) Lkasr: large kernel attention for lightweight image super-resolution. Knowl Based Syst 252:109376","journal-title":"Knowl Based Syst"},{"key":"5286_CR42","unstructured":"Liu X, Shen F, Zhao J, Nie C (2022) Randommix: a mixed sample data augmentation method with multiple mixed modes. arXiv preprint arXiv:2205.08728"},{"key":"5286_CR43","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European Conference on Computer Vision, pp 630\u2013645. Springer","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"5286_CR44","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"5286_CR45","unstructured":"Agarap AF (2018) Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375"},{"key":"5286_CR46","doi-asserted-by":"crossref","unstructured":"Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), vol 2, pp 1735\u20131742. IEEE","DOI":"10.1109\/CVPR.2006.100"},{"key":"5286_CR47","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"key":"5286_CR48","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Dong C, Loy CC (2018) Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 606\u2013615","DOI":"10.1109\/CVPR.2018.00070"},{"key":"5286_CR49","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"5286_CR50","unstructured":"Contributors M (2018) MMCV: openMMLab computer vision foundation. https:\/\/github.com\/open-mmlab\/mmcv"},{"key":"5286_CR51","doi-asserted-by":"crossref","unstructured":"Wang Z, Cun X, Bao J, Zhou W, Liu J, Li H (2022) Uformer: a general u-shaped transformer for image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 17683\u201317693","DOI":"10.1109\/CVPR52688.2022.01716"},{"issue":"4","key":"5286_CR52","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"5286_CR53","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"A Mittal","year":"2012","unstructured":"Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695\u20134708","journal-title":"IEEE Trans Image Process"},{"key":"5286_CR54","unstructured":"OpenAI: GPT-4 technical report (2023)"},{"key":"5286_CR55","doi-asserted-by":"publisher","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp 3354\u20133361. https:\/\/doi.org\/10.1109\/CVPR.2012.6248074","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"5286_CR56","doi-asserted-by":"crossref","unstructured":"Menze M, Geiger A (2015) Object scene flow for autonomous vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3061\u20133070","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"5286_CR57","doi-asserted-by":"publisher","first-page":"7608","DOI":"10.1109\/TIP.2021.3108019","volume":"30","author":"K Zhang","year":"2021","unstructured":"Zhang K, Li D, Luo W, Ren W (2021) Dual attention-in-attention model for joint rain streak and raindrop removal. IEEE Trans Image Process 30:7608\u20137619","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"5286_CR58","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1109\/TPAMI.2022.3148707","volume":"45","author":"K Zhang","year":"2022","unstructured":"Zhang K, Li D, Luo W, Ren W, Liu W (2022) Enhanced spatio-temporal interaction learning for video deraining: faster and better. IEEE Trans Pattern Anal Machine Intell 45(1):1287\u20131293","journal-title":"IEEE Trans Pattern Anal Machine Intell"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05286-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05286-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05286-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T22:39:33Z","timestamp":1729291173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05286-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,20]]},"references-count":58,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["5286"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05286-0","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,20]]},"assertion":[{"value":"10 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}