{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,11]],"date-time":"2025-05-11T04:01:54Z","timestamp":1746936114379,"version":"3.40.5"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"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":["62271354"],"award-info":[{"award-number":["62271354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11263-025-02364-z","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T09:39:35Z","timestamp":1738143575000},"page":"3762-3780","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-supervised Shutter Unrolling with Events"],"prefix":"10.1007","volume":"133","author":[{"given":"Mingyuan","family":"Lin","sequence":"first","affiliation":[]},{"given":"Yangguang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Boxin","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Chu","family":"He","sequence":"additional","affiliation":[]},{"given":"Gui-song","family":"Xia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7329-4631","authenticated-orcid":false,"given":"Lei","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"2364_CR1","doi-asserted-by":"crossref","unstructured":"Albl, C., Kukelova, Z., Larsson, V., Polic, M., Pajdla, T., & Schindler, K. (2020). From two rolling shutters to one global shutter. In: CVPR, pp 2505\u20132513","DOI":"10.1109\/CVPR42600.2020.00258"},{"key":"2364_CR2","doi-asserted-by":"crossref","unstructured":"Baker, S., Bennett, E., Kang, S.B., & Szeliski, R. (2010). Removing rolling shutter wobble. In: CVPR, pp 2392\u20132399","DOI":"10.1109\/CVPR.2010.5539932"},{"key":"2364_CR3","doi-asserted-by":"crossref","unstructured":"Bao, W., Lai, W.S., Ma, C., Zhang, X., Gao, Z., & Yang, M.H. (2019a). Depth-aware video frame interpolation. In: CVPR, pp 3703\u20133712","DOI":"10.1109\/CVPR.2019.00382"},{"issue":"3","key":"2364_CR4","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1109\/TPAMI.2019.2941941","volume":"43","author":"W Bao","year":"2019","unstructured":"Bao, W., Lai, W. S., Zhang, X., Gao, Z., & Yang, M. H. (2019). Memc-net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE TPAMI, 43(3), 933\u2013948.","journal-title":"IEEE TPAMI"},{"issue":"10","key":"2364_CR5","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.1109\/JSSC.2014.2342715","volume":"49","author":"C Brandli","year":"2014","unstructured":"Brandli, C., Berner, R., Yang, M., Liu, S. C., & Delbruck, T. (2014). A 240$$\\times $$ 180 130 db 3 $$\\mu $$s latency global shutter spatiotemporal vision sensor. IEEE J Solid-State Circuits, 49(10), 2333\u20132341.","journal-title":"IEEE J Solid-State Circuits"},{"key":"2364_CR6","doi-asserted-by":"crossref","unstructured":"Cho, H., Jeong, Y., Kim ,T., & Yoon, K.J. (2023). Non-coaxial event-guided motion deblurring with spatial alignment. In: ICCV, pp 12492\u201312503","DOI":"10.1109\/ICCV51070.2023.01148"},{"key":"2364_CR7","doi-asserted-by":"crossref","unstructured":"Choi, J., Wong, C. W., Su, H., & Wu, M. (2022). Analysis of ENF Signal Extraction From Videos Acquired by Rolling Shutters. TechRxiv","DOI":"10.36227\/techrxiv.21300960.v1"},{"key":"2364_CR8","unstructured":"Delbruck, T., Hu,Y., & He, Z. (2021). V2e: From video frames to realistic dvs event camera streams. In: CVPRW"},{"key":"2364_CR9","doi-asserted-by":"crossref","unstructured":"Erbach, J., Tulyakov, S., Vitoria, P., Bochicchio, A., & Li, Y. (2023). Evshutter: Transforming events for unconstrained rolling shutter correction. In: CVPR, pp 13904\u201313913","DOI":"10.1109\/CVPR52729.2023.01336"},{"key":"2364_CR10","doi-asserted-by":"crossref","unstructured":"Fan, B., & Dai, Y. (2021). Inverting a rolling shutter camera: bring rolling shutter images to high framerate global shutter video. In: ICCV, pp 4228\u20134237","DOI":"10.1109\/ICCV48922.2021.00419"},{"key":"2364_CR11","doi-asserted-by":"crossref","unstructured":"Fan, B., Dai, Y., & He, M. (2021). Sunet: symmetric undistortion network for rolling shutter correction. In: ICCV, pp 4541\u20134550","DOI":"10.1109\/ICCV48922.2021.00450"},{"key":"2364_CR12","doi-asserted-by":"crossref","unstructured":"Fan, B., Dai, Y., Zhang, Z., Liu, Q., & He, M. (2022). Context-aware video reconstruction for rolling shutter cameras. In: CVPR, pp 17572\u201317582","DOI":"10.1109\/CVPR52688.2022.01705"},{"issue":"6","key":"2364_CR13","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381\u2013395.","journal-title":"Communications of the ACM"},{"issue":"1","key":"2364_CR14","first-page":"154","volume":"44","author":"G Gallego","year":"2020","unstructured":"Gallego, G., Delbr\u00fcck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2020). Event-based vision: A survey. IEEE TPAMI, 44(1), 154\u2013180.","journal-title":"Event-based vision: A survey. IEEE TPAMI"},{"key":"2364_CR15","doi-asserted-by":"crossref","unstructured":"Grundmann, M., Kwatra, V., Castro, D., & Essa, I. (2012). Calibration-free rolling shutter removal. In: 2012 IEEE International Conference on Computational Photography (ICCP), pp 1\u20138","DOI":"10.1109\/ICCPhot.2012.6215213"},{"key":"2364_CR16","doi-asserted-by":"crossref","unstructured":"He, W., You, K., Qiao, Z., Jia, X., Zhang, Z., Wang, W., Lu, H., Wang, Y., & Liao, J. (2022). Timereplayer: Unlocking the potential of event cameras for video interpolation. In: CVPR, pp 17804\u201317813","DOI":"10.1109\/CVPR52688.2022.01728"},{"key":"2364_CR17","unstructured":"IniVation (2020). Understanding the performance of neuromorphic event-based vision sensors. https:\/\/inivationcom\/"},{"key":"2364_CR18","doi-asserted-by":"crossref","unstructured":"Jiang, H., Sun, D., Jampani, V., Yang, M. H., Learned-Miller, E., & Kautz, J. (2018). Super slomo: High quality estimation of multiple intermediate frames for video interpolation. In: CVPR, pp 9000\u20139008","DOI":"10.1109\/CVPR.2018.00938"},{"key":"2364_CR19","doi-asserted-by":"crossref","unstructured":"Jin, M., Hu, Z., & Favaro, P. (2019). Learning to extract flawless slow motion from blurry videos. In: CVPR, pp 8112\u20138121","DOI":"10.1109\/CVPR.2019.00830"},{"key":"2364_CR20","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980"},{"key":"2364_CR21","doi-asserted-by":"crossref","unstructured":"Lao, Y., & Ait-Aider, O. (2018). A robust method for strong rolling shutter effects correction using lines with automatic feature selection. In: CVPR, pp 4795\u20134803","DOI":"10.1109\/CVPR.2018.00504"},{"key":"2364_CR22","doi-asserted-by":"crossref","unstructured":"Li, M., Wang, P., Zhao, L., Liao, B., & Liu, P. (2024). Usb-nerf: Unrolling shutter bundle adjusted neural radiance fields. In: ICLR","DOI":"10.1109\/CVPR52729.2023.00406"},{"issue":"2","key":"2364_CR23","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/JSSC.2007.914337","volume":"43","author":"P Lichtsteiner","year":"2008","unstructured":"Lichtsteiner, P., Posch, C., & Delbruck, T. (2008). A 128$$\\times $$128 120 dB 15 $$\\mu $$s latency asynchronous temporal contrast vision sensor. IEEE J Solid-State Circuits, 43(2), 566\u2013576. https:\/\/doi.org\/10.1109\/JSSC.2007.914337","journal-title":"IEEE J Solid-State Circuits"},{"key":"2364_CR24","doi-asserted-by":"crossref","unstructured":"Lin, S., Zhang, J., Pan, J., Jiang, Z., Zou, D., Wang, Y., Chen, J., & Ren, J. (2020). Learning event-driven video deblurring and interpolation. In: ECCV, pp 695\u2013710","DOI":"10.1007\/978-3-030-58598-3_41"},{"key":"2364_CR25","first-page":"709","volume":"32","author":"X Lin","year":"2023","unstructured":"Lin, X., Li, Y., Zhu, J., & Zeng, H. (2023). Deflickercyclegan: Learning to detect and remove flickers in a single image. IEEE TIP, 32, 709\u2013720.","journal-title":"IEEE TIP"},{"key":"2364_CR26","doi-asserted-by":"crossref","unstructured":"Liu, P., Cui, Z., Larsson, V., & Pollefeys, M .(2020). Deep shutter unrolling network. In: CVPR, pp 5941\u20135949","DOI":"10.1109\/CVPR42600.2020.00598"},{"issue":"4","key":"2364_CR27","first-page":"1","volume":"32","author":"S Liu","year":"2013","unstructured":"Liu, S., Yuan, L., Tan, P., & Sun, J. (2013). Bundled camera paths for video stabilization. ACM TOG, 32(4), 1\u201310.","journal-title":"ACM TOG"},{"key":"2364_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yeh, R.A., Tang, X., Liu, Y., & Agarwala, A. (2017). Video frame synthesis using deep voxel flow. In: ICCV, pp 4463\u20134471","DOI":"10.1109\/ICCV.2017.478"},{"key":"2364_CR29","unstructured":"Loshchilov, I., & Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv:1608.03983"},{"key":"2364_CR30","unstructured":"Lu, Y., Liang, G., & Wang, L. (2023). Self-supervised learning of event-guided video frame interpolation for rolling shutter frames. arXiv:2306.15507"},{"key":"2364_CR31","doi-asserted-by":"crossref","unstructured":"Niklaus, S., Mai, L., & Liu, F. (2017a). Video frame interpolation via adaptive convolution. In: CVPR, pp 670\u2013679","DOI":"10.1109\/CVPR.2017.244"},{"key":"2364_CR32","doi-asserted-by":"crossref","unstructured":"Niklaus, S., Mai, L., & Liu, F. (2017b). Video frame interpolation via adaptive separable convolution. In: ICCV, pp 261\u2013270","DOI":"10.1109\/ICCV.2017.37"},{"key":"2364_CR33","doi-asserted-by":"crossref","unstructured":"Niu, M., Chen, T., Zhan, Y., Li, Z., Ji, X., & Zheng, Y. (2025). Rs-nerf: Neural radiance fields from rolling shutter images. In: ECCV, pp 163\u2013180","DOI":"10.1007\/978-3-031-72952-2_10"},{"key":"2364_CR34","doi-asserted-by":"crossref","unstructured":"Oth, L., Furgale, P., Kneip, L., & Siegwart, R. (2013). Rolling shutter camera calibration. In: CVPR, pp 1360\u20131367","DOI":"10.1109\/CVPR.2013.179"},{"key":"2364_CR35","doi-asserted-by":"crossref","unstructured":"Pan, L., Scheerlinck, C., Yu, X., Hartley, R., Liu, M., & Dai, Y. (2019). Bringing a blurry frame alive at high frame-rate with an event camera. In: CVPR, pp 6820\u20136829","DOI":"10.1109\/CVPR.2019.00698"},{"key":"2364_CR36","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, & N., Antiga, L., et\u00a0al (2019). Pytorch: An imperative style, high-performance deep learning library. In: NeurIPS"},{"key":"2364_CR37","doi-asserted-by":"crossref","unstructured":"Purkait, P., & Zach, C. (2018). Minimal solvers for monocular rolling shutter compensation under ackermann motion. In: WACV, pp 903\u2013911","DOI":"10.1109\/WACV.2018.00104"},{"key":"2364_CR38","doi-asserted-by":"crossref","unstructured":"Purkait, P., Zach, C., Leonardis, A. (2017). Rolling shutter correction in manhattan world. In: ICCV, pp 882\u2013890","DOI":"10.1109\/ICCV.2017.101"},{"key":"2364_CR39","unstructured":"Rebecq, H., Gehrig, D., & Scaramuzza, D. (2018). Esim: an open event camera simulator. In: Conference on Robot Learning, pp 969\u2013982"},{"key":"2364_CR40","doi-asserted-by":"crossref","unstructured":"Reda, F.A., Sun, D., Dundar, A., Shoeybi, M., Liu, G., Shih, K.J., Tao, A., Kautz, J., & Catanzaro, B. (2019). Unsupervised video interpolation using cycle consistency. In: ICCV, pp 892\u2013900","DOI":"10.1109\/ICCV.2019.00098"},{"key":"2364_CR41","doi-asserted-by":"crossref","unstructured":"Rengarajan, V., Rajagopalan, A.N., & Aravind, R. (2016). From bows to arrows: Rolling shutter rectification of urban scenes. In: CVPR, pp 2773\u20132781","DOI":"10.1109\/CVPR.2016.303"},{"key":"2364_CR42","doi-asserted-by":"crossref","unstructured":"Rengarajan, V., Balaji, Y., & Rajagopalan, A. (2017). Unrolling the shutter: Cnn to correct motion distortions. In: CVPR, pp 2291\u20132299","DOI":"10.1109\/CVPR.2017.252"},{"key":"2364_CR43","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2364_CR44","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., & Frahm, J.M. (2016). Structure-from-motion revisited. In: CVPR, pp 4104\u20134113","DOI":"10.1109\/CVPR.2016.445"},{"key":"2364_CR45","doi-asserted-by":"crossref","unstructured":"Sheinin, M., Chan, D., O\u2019Toole ,M., & Narasimhan, S.G. (2022). Dual-shutter optical vibration sensing. In: CVPR, pp 16324\u201316333","DOI":"10.1109\/CVPR52688.2022.01584"},{"key":"2364_CR46","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., & Kautz, J. (2018). Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: CVPR, pp 8934\u20138943","DOI":"10.1109\/CVPR.2018.00931"},{"key":"2364_CR47","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., Gehrig, D., Georgoulis, S., Erbach, J., Gehrig, M., Li, Y., & Scaramuzza, D. (2021). Time lens: Event-based video frame interpolation. In: CVPR, pp 16155\u201316164","DOI":"10.1109\/CVPR46437.2021.01589"},{"key":"2364_CR48","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., Bochicchio, A., Gehrig, D., Georgoulis, S., Li, Y., & Scaramuzza, D. (2022). Time lens++: Event-based frame interpolation with parametric non-linear flow and multi-scale fusion. In: CVPR, pp 17755\u201317764","DOI":"10.1109\/CVPR52688.2022.01723"},{"key":"2364_CR49","doi-asserted-by":"crossref","unstructured":"Wang, B., He,J., Yu, L., Xia, G.S., & Yang, W. (2020). Event enhanced high-quality image recovery. In: ECCV, pp 155\u2013171","DOI":"10.1007\/978-3-030-58601-0_10"},{"key":"2364_CR50","doi-asserted-by":"crossref","unstructured":"Xu, F., Yu, L., Wang, B., Yang, W., Xia, G.S., Jia, X., Qiao, Z., & Liu, J. (2021). Motion deblurring with real events. In: ICCV, pp 2583\u20132592","DOI":"10.1109\/ICCV48922.2021.00258"},{"key":"2364_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, D., Ding, Q., Duan, P., Zhou, C., & Shi, B. (2022). Data association between event streams and intensity frames under diverse baselines. In: ECCV, pp 72\u201390","DOI":"10.1007\/978-3-031-20071-7_5"},{"key":"2364_CR52","doi-asserted-by":"crossref","unstructured":"Zhang, X., & Yu, L. (2022). Unifying motion deblurring and frame interpolation with events. In: CVPR, pp 17765\u201317774","DOI":"10.1109\/CVPR52688.2022.01724"},{"key":"2364_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2024.104094","volume":"248","author":"X Zhang","year":"2024","unstructured":"Zhang, X., Huang, H., Jia, X., Wang, D., Zhang, L., Zheng, B., Zhou, W., & Lu, H. (2024). Neural image re-exposure. Computer Vision and Image Understanding, 248, 104094.","journal-title":"Computer Vision and Image Understanding"},{"key":"2364_CR54","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, Y., & Sato, I. (2021). Towards rolling shutter correction and deblurring in dynamic scenes. In: CVPR, pp 9219\u20139228","DOI":"10.1109\/CVPR46437.2021.00910"},{"key":"2364_CR55","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Cao, M., Sun, X., Wu, Z., Zhou, Z., Zheng, Y., Lin, S., & Sato, I. (2022). Bringing rolling shutter images alive with dual reversed distortion. In: ECCV, pp 233\u2013249","DOI":"10.1007\/978-3-031-20071-7_14"},{"key":"2364_CR56","doi-asserted-by":"crossref","unstructured":"Zho, X., Duan, P., Ma, Y., & Shi, B. (2022). Evunroll: Neuromorphic events based rolling shutter image correction. In: CVPR, pp 17775\u201317784","DOI":"10.1109\/CVPR52688.2022.01725"},{"key":"2364_CR57","doi-asserted-by":"publisher","unstructured":"Zhu, A., Yuan, L., Chaney, K., & Daniilidis, K. (2018). EV-flowNet: Self-supervised optical flow estimation for event-based cameras. In: Proceedings of Robotics: Science and Systems, Pittsburgh, Pennsylvania. https:\/\/doi.org\/10.15607\/RSS.2018.XIV.062","DOI":"10.15607\/RSS.2018.XIV.062"},{"key":"2364_CR58","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., & Efros, A.A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"},{"key":"2364_CR59","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Cheong, L.F., Hee\u00a0Lee, G. (2017). Rolling-shutter-aware differential sfm and image rectification. In: ICCV, pp 948\u2013956","DOI":"10.1109\/ICCV.2017.108"},{"key":"2364_CR60","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Tran, Q.H., Ji, P., Cheong, L.F., & Chandraker, M. (2019). Learning structure-and-motion-aware rolling shutter correction. In: CVPR, pp 4551\u20134560","DOI":"10.1109\/CVPR.2019.00468"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02364-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-025-02364-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02364-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T06:57:22Z","timestamp":1746860242000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-025-02364-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,29]]},"references-count":60,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2364"],"URL":"https:\/\/doi.org\/10.1007\/s11263-025-02364-z","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"type":"print","value":"0920-5691"},{"type":"electronic","value":"1573-1405"}],"subject":[],"published":{"date-parts":[[2025,1,29]]},"assertion":[{"value":"13 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}