{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:43:48Z","timestamp":1778049828571,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Guangdong Province Key R&D projects","award":["2019B010154002"],"award-info":[{"award-number":["2019B010154002"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s11760-025-03945-8","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T13:08:16Z","timestamp":1741180096000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Global-regional-local multilevel lightweight attention modeling for event-based efficient video reconstruction"],"prefix":"10.1007","volume":"19","author":[{"given":"Ziyu","family":"Nie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongdong","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lilin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"3945_CR1","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TPAMI.2020.3008413","volume":"44","author":"G Gallego","year":"2022","unstructured":"Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A., Conradt, J., Daniilidis, K., Scaramuzza, D.: Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44, 154\u2013180 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2020.3008413","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3945_CR2","doi-asserted-by":"crossref","unstructured":"Tomy, A., Paigwar, A., Mann, K.S., Renzaglia, A., Laugier, C.: Fusing event-based and RGB camera for robust object detection in adverse conditions. In: 2022 International Conference on Robotics and Automation (ICRA). pp. 933\u2013939 (2022)","DOI":"10.1109\/ICRA46639.2022.9812059"},{"key":"3945_CR3","doi-asserted-by":"crossref","unstructured":"Safa, A., Verbelen, T., Ocket, I., Bourdoux, A., Sahli, H., Catthoor, F., Gielen, G.: Fusing event-based camera and radar for SLAM using spiking neural networks with continual STDP learning. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 2782\u20132788 (2023)","DOI":"10.1109\/ICRA48891.2023.10160681"},{"key":"3945_CR4","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.1109\/TPAMI.2019.2963386","volume":"43","author":"H Rebecq","year":"2019","unstructured":"Rebecq, H., Ranftl, R., Koltun, V., Scaramuzza, D.: High speed and high dynamic range video with an event camera. IEEE Trans. Pattern Anal. Mach. Intell. 43, 1964\u20131980 (2019). https:\/\/doi.org\/10.1109\/TPAMI.2019.2963386","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3945_CR5","doi-asserted-by":"crossref","unstructured":"Cadena, P.R.G., Qian, Y., Wang, C., Yang, M.: Sparse-E2VID: a sparse convolutional model for event-based video reconstruction trained with real event noise. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 4150\u20134158 (2023)","DOI":"10.1109\/CVPRW59228.2023.00437"},{"key":"3945_CR6","doi-asserted-by":"crossref","unstructured":"Weng, W., Zhang, Y., Xiong, Z.: Event-based video reconstruction using transformer. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). pp. 2543\u20132552. IEEE, Montreal, QC, Canada (2021)","DOI":"10.1109\/ICCV48922.2021.00256"},{"key":"3945_CR7","doi-asserted-by":"publisher","first-page":"1826","DOI":"10.1109\/TIP.2024.3372460","volume":"33","author":"B Ercan","year":"2024","unstructured":"Ercan, B., Eker, O., Saglam, C., Erdem, A., Erdem, E.: HyperE2VID: improving event-based video reconstruction via hypernetworks. IEEE Trans. Image Process. 33, 1826\u20131837 (2024). https:\/\/doi.org\/10.1109\/TIP.2024.3372460","journal-title":"IEEE Trans. Image Process."},{"key":"3945_CR8","doi-asserted-by":"crossref","unstructured":"Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N., Mahony, R.E., Scaramuzza, D.: Fast image reconstruction with an event camera. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 156\u2013163. IEEE, Snowmass Village, CO, USA (2020)","DOI":"10.1109\/WACV45572.2020.9093366"},{"key":"3945_CR9","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). pp. 9992\u201310002. IEEE, Montreal, QC, Canada (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3945_CR10","doi-asserted-by":"crossref","unstructured":"Li, Y., Fan, Y., Xiang, X., Demandolx, D., Ranjan, R., Timofte, R., Van Gool, L.: Efficient and explicit modelling of image hierarchies for image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 18278\u201318289 (2023)","DOI":"10.1109\/CVPR52729.2023.01753"},{"key":"3945_CR11","doi-asserted-by":"publisher","first-page":"2488","DOI":"10.1109\/TIP.2021.3052070","volume":"30","author":"PRG Cadena","year":"2021","unstructured":"Cadena, P.R.G., Qian, Y., Wang, C., Yang, M.: SPADE-E2VID: spatially-adaptive denormalization for event-based video reconstruction. IEEE Trans. on Image Process. 30, 2488\u20132500 (2021). https:\/\/doi.org\/10.1109\/TIP.2021.3052070","journal-title":"IEEE Trans. on Image Process."},{"key":"3945_CR12","doi-asserted-by":"crossref","unstructured":"Paredes-Valles, F., De Croon, G.C.H.E.: Back to event basics: self-supervised learning of image reconstruction for event cameras via photometric constancy. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3445\u20133454. IEEE, Nashville, TN, USA (2021)","DOI":"10.1109\/CVPR46437.2021.00345"},{"key":"3945_CR13","doi-asserted-by":"crossref","unstructured":"Stoffregen, T., Scheerlinck, C., Scaramuzza, D., Drummond, T., Barnes, N., Kleeman, L., Mahony, R.: Reducing the sim-to-real gap for event cameras. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXVII 16. pp. 534\u2013549. Springer (2020)","DOI":"10.1007\/978-3-030-58583-9_32"},{"key":"3945_CR14","doi-asserted-by":"crossref","unstructured":"Rebecq, H., Ranftl, R., Koltun, V., Scaramuzza, D.: Events-to-video: bringing modern computer vision to event cameras. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3857\u20133866 (2019)","DOI":"10.1109\/CVPR.2019.00398"},{"key":"3945_CR15","doi-asserted-by":"crossref","unstructured":"Cook, M., Gugelmann, L., Jug, F., Krautz, C., Steger, A.: Interacting maps for fast visual interpretation. In: The 2011 International Joint Conference on Neural Networks. pp. 770\u2013776. IEEE, San Jose, CA, USA (2011)","DOI":"10.1109\/IJCNN.2011.6033299"},{"key":"3945_CR16","unstructured":"Scheerlinck, C., Barnes, N., Mahony, R.: Continuous-time Intensity Estimation Using Event Cameras, http:\/\/arxiv.org\/abs\/1811.00386 (2018)"},{"key":"3945_CR17","doi-asserted-by":"publisher","first-page":"2519","DOI":"10.1109\/TPAMI.2020.3036667","volume":"44","author":"L Pan","year":"2020","unstructured":"Pan, L., Hartley, R., Scheerlinck, C., Liu, M., Yu, X., Dai, Y.: High frame rate video reconstruction based on an event camera. IEEE Trans. Pattern Anal. Mach. Intell. 44, 2519\u20132533 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2020.3036667","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3945_CR18","doi-asserted-by":"crossref","unstructured":"Lu, Y., Shi, D., Li, R., Zhang, Y., Jing, L., Yang, S.: SCSE-E2VID: Improved event-based video reconstruction with an event camera. In: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). pp. 3249\u20133254. IEEE (2022)","DOI":"10.1109\/SMC53654.2022.9945237"},{"key":"3945_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, L., Wang, X., Chang, Y., Li, J., Huang, T., Tian, Y.: Event-based video reconstruction via potential-assisted spiking neural network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3594\u20133604 (2022)","DOI":"10.1109\/CVPR52688.2022.00358"},{"key":"3945_CR20","doi-asserted-by":"crossref","unstructured":"Zihao Zhu, A., Yuan, L., Chaney, K., Daniilidis, K.: Unsupervised event-based optical flow using motion compensation. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops. pp. 0\u20130 (2018)","DOI":"10.1007\/978-3-030-11024-6_54"},{"key":"3945_CR21","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1007\/978-3-031-19797-0_24","volume-title":"Computer Vision ECCV 2022","author":"L Sun","year":"2022","unstructured":"Sun, L., Sakaridis, C., Liang, J., Jiang, Q., Yang, K., Sun, P., Ye, Y., Wang, K., Gool, L.V.: Event-based fusion for motion deblurring with cross-modal attention. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision ECCV 2022, pp. 412\u2013428. Springer Nature Switzerland, Cham (2022)"},{"key":"3945_CR22","doi-asserted-by":"crossref","unstructured":"Gehrig, D., Loquercio, A., Derpanis, K., Scaramuzza, D.: End-to-end learning of representations for asynchronous event-based data. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV). pp. 5632\u20135642. IEEE, Seoul, Korea (South) (2019)","DOI":"10.1109\/ICCV.2019.00573"},{"key":"3945_CR23","doi-asserted-by":"crossref","unstructured":"Nam, Y., Mostafavi, M., Yoon, K.J., Choi, J.: Stereo depth from events cameras: concentrate and focus on the future. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 6114\u20136123 (2022)","DOI":"10.1109\/CVPR52688.2022.00602"},{"key":"3945_CR24","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"3945_CR25","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European conference on computer vision. pp. 213\u2013229. Springer (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"3945_CR26","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"3945_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., others: Swin transformer v2: Scaling up capacity and resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 12009\u201312019 (2022)","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"3945_CR28","unstructured":"Rebecq, H., Gehrig, D., Scaramuzza, D.: ESIM: an open event camera simulator. In: Conference on robot learning. pp. 969\u2013982. PMLR (2018)"},{"key":"3945_CR29","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part V 13. pp. 740\u2013755. Springer (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"3945_CR30","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1177\/0278364917691115","volume":"36","author":"E Mueggler","year":"2017","unstructured":"Mueggler, E., Rebecq, H., Gallego, G., Delbruck, T., Scaramuzza, D.: The event-camera dataset and simulator: event-based data for pose estimation, visual odometry, and SLAM. Int. J. Robot. Res. 36, 142\u2013149 (2017)","journal-title":"Int. J. Robot. Res."},{"key":"3945_CR31","doi-asserted-by":"publisher","first-page":"2032","DOI":"10.1109\/LRA.2018.2800793","volume":"3","author":"AZ Zhu","year":"2018","unstructured":"Zhu, A.Z., Thakur, D., \u00d6zaslan, T., Pfrommer, B., Kumar, V., Daniilidis, K.: The multivehicle stereo event camera dataset: an event camera dataset for 3D perception. IEEE Robot. Autom. Lett. 3, 2032\u20132039 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"3945_CR32","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"3945_CR33","doi-asserted-by":"crossref","unstructured":"Ercan, B., Eker, O., Erdem, A., Erdem, E.: Evreal: Towards a comprehensive benchmark and analysis suite for event-based video reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3943\u20133952 (2023)","DOI":"10.1109\/CVPRW59228.2023.00410"},{"key":"3945_CR34","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"3945_CR35","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.3390\/make5040083","volume":"5","author":"J Terven","year":"2023","unstructured":"Terven, J., C\u00f3rdova-Esparza, D.-M., Romero-Gonz\u00e1lez, J.-A.: A comprehensive review of yolo architectures in computer vision: from yolov1 to yolov8 and yolo-nas. Mach. Learn. Knowl. Extr. 5, 1680\u20131716 (2023)","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"3945_CR36","unstructured":"Omni-Kernel Network for Image Restoration | Proceedings of the AAAI Conference on Artificial Intelligence, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/27907"},{"key":"3945_CR37","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3354982","author":"M Zhang","year":"2024","unstructured":"Zhang, M., Bai, H., Shang, W., Guo, J., Li, Y., Gao, X.: MDEformer: mixed difference equation inspired transformer for compressed video quality enhancement. IEEE Trans. Neural Netw. Learn. Syst. (2024). https:\/\/doi.org\/10.1109\/TNNLS.2024.3354982","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3945_CR38","doi-asserted-by":"publisher","first-page":"103819","DOI":"10.1016\/j.imavis.2019.10.005","volume":"92","author":"Y Li","year":"2019","unstructured":"Li, Y., Zhang, D., Lee, D.-J.: IIRNet: a lightweight deep neural network using intensely inverted residuals for image recognition. Image Vis. Comput. 92, 103819 (2019). https:\/\/doi.org\/10.1016\/j.imavis.2019.10.005","journal-title":"Image Vis. Comput."},{"key":"3945_CR39","doi-asserted-by":"publisher","first-page":"2623","DOI":"10.1109\/TNNLS.2019.2933590","volume":"31","author":"M Zhang","year":"2020","unstructured":"Zhang, M., Wang, N., Li, Y., Gao, X.: Neural probabilistic graphical model for face sketch synthesis. IEEE Trans. Neural Netw. Learn. Syst. 31, 2623\u20132637 (2020). https:\/\/doi.org\/10.1109\/TNNLS.2019.2933590","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3945_CR40","doi-asserted-by":"publisher","first-page":"9423","DOI":"10.1109\/TPAMI.2024.3419007","volume":"46","author":"Y Cui","year":"2024","unstructured":"Cui, Y., Ren, W., Cao, X., Knoll, A.: Revitalizing convolutional network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 46, 9423\u20139438 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2024.3419007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3945_CR41","unstructured":"Cui, Y., Tao, Y., Bing, Z., Ren, W., Gao, X., Cao, X., Huang, K., Knoll, A.: Selective Frequency Network for Image Restoration. Presented at the 11th International Conference on Learning Representations (2022)"},{"key":"3945_CR42","doi-asserted-by":"publisher","unstructured":"Cui, Y., Ren, W., Cao, X., Knoll, A.: Focal Network for Image Restoration. https:\/\/doi.org\/10.1109\/ICCV51070.2023.01195","DOI":"10.1109\/ICCV51070.2023.01195"},{"key":"3945_CR43","doi-asserted-by":"publisher","first-page":"3109","DOI":"10.1109\/TNNLS.2018.2890017","volume":"30","author":"M Zhang","year":"2019","unstructured":"Zhang, M., Wang, N., Li, Y., Gao, X.: Deep latent low-rank representation for face sketch synthesis. IEEE Trans. Neural Netw. Learn. Syst. 30, 3109\u20133123 (2019). https:\/\/doi.org\/10.1109\/TNNLS.2018.2890017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03945-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-03945-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03945-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T20:07:09Z","timestamp":1744142829000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-03945-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,5]]},"references-count":43,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["3945"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-03945-8","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,5]]},"assertion":[{"value":"9 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","order":4,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"358"}}